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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 December 10, 2025, has been entered.
Response to Amendment
Claims 1, 10, and 12-19 have been amended. Claims 8, 9, and 11 have been canceled. Claims 1-7, 10, and 12-19 are pending and are provided to be examined upon their merits.
Response to Arguments
Applicant’s arguments with respect to claims 1-7, 10, and 12-19 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. A response is provided below in bold where appropriate.
Applicant notes Claim Interpretation, pg. 9 of Remarks:
I. Claim Interpretation
The Examiner stated that "claims 10 recites "...the one or more instance of computer- readable media not constituting a transitory propagating data signal, ..." For examination purposes, this is interpreted to be equivalent to claiming non-transitory computer readable media."
Claim 10 and 12-18 have been amended to recite "non-transitory computer-readable media," as the Examiner suggested. See Office Action at 10.
It is respectfully submitted that the foregoing amendment addresses the interpretation, and therefore requests reconsideration of the claims.
Noted and the interpretation is removed based on the amendments.
Applicant argues 35 USC §101 Rejection, starting pg. 9 of Remarks:
II. The Rejection Of The Claims Under 35 U.S.C. 101
Claims 1-7, 10, and 12-19 stand rejected under 35 U.S.C. § 101 because the claimed combinations are allegedly directed to an abstract idea without significantly more. Claims 1, 10, and 19 are independent. This rejection is respectfully traversed for the following reasons.
More specifically, the Office indicates that "if a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as managing interactions between people or managing personal behavior, then it falls within the "Certain Methods of Organizing Human Activity" grouping of abstract ideas." See Office Action at 12. The Office concludes that "dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination." See Office Action at 15.
Applicant respectfully disagrees. Applicant submits that, on August 4, 2025, the United States Patent and Trademark Office (USPTO) released a memorandum to patent examiners further clarifying its position on subject matter eligibility. See 2025 Revised Patent Subject Matter Eligibility Guidance (https://www.uspto.gov/sites/default/files/documents/memo-101- 20250804.pdf,"August 2025 Memo" hereinafter). Applicant respectfully submits that claims 1, 10, and 19 for executing computer programs and/or training or applying machine learning models (as described in at least at paragraphs [0019]-[0020] and [0026]-[0028] and claims 1, 10, and 19 of the published application) are not directed to an abstract idea, under Step 2a prong 1 of the flowchart provided in MPEP 2106 subsection III, according to the August 2025 Memo that revise the procedures for determining whether a claim is directed to a judicial exception. The August 2025 Memo directs examiners to consider whether software-related arts including Artificial Intelligence (AI) and Machine Learning function may be performed in a human mind before declaring the patent an ineligible mental process, stating:
"The mental process grouping is not without limits. Examiners are reminded not to expand this grouping in a manner that encompasses claim limitations that cannot practically be performed in the human mind. The MPEP and the AI-SME Update provide examples of claim limitations that cannot be practically performed in the human mind. Claim limitations that encompass AI in a way that cannot be practically performed in the human mind do not fall within this grouping."
USPTO, Memorandum to Tech. Centers 2100, 2600, and 3600, p. 2 (August 4, 2025) (emphasis added).
The machine learning itself is not considered abstract as a mental process. It is not enough, however, to make abstract claims statutory. For example, there is no claim limitations that recited details of how machine learning is actually performed or indications of any improvement to machine learning technology itself.
Applicant further submits that under Step 2a prong 2 of the flowchart provided in MPEP 2106 subsection III, the August 2025 Memo clarifies that explicit explanation of a resulting technological improvement does not need to be in the specification for an abstract idea to be incorporated into a practical application. Specifically, the August 2025 Memo states: "[t]he specification does not need to explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art." August 2025 Memo, p. 4.
Respectfully, Applicant should be citing their specification teachings that teach an improvement to machine learning that would be apparent to one or ordinary skilled in the art.
From Applicant’s specification:
“In various embodiments, the facility uses a variety of machine learning model types. In some embodiments, the facility uses a transformer-based machine learning model, such as those described in any of the following, each of which is hereby incorporated by reference in its entirety: "BERT" (available at huggingface.co/docs/transformers/model_doc/bert); J. Devlin, M.W. Chang, K.
25 Lee, K. Toutanova, "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding," arxiv: 1810.04805 (available at arxiv.org/abs/1810.04805); B. Portelli, "DiLBERT (Disease Language BERT)" (available at huggingface.co/beatrice-portelli/DiLBERT); W. Siblini, M. Challal, C. Pasqual, "Delaying Interaction Layers in Transformer-based Encoders for Efficient Open Domain Question Answering," arxiv: 2010.08422 (available at arxiv.org/abs/2010.08422); and K. Roitero, B. Portelli, M.H. Popescu and VD. Mea, "DiLBERT: Cheap Embeddings for Disease Related Medical NLP," in IEEE Access, vol. 9, pp. 159714-159723, 2021, doi: 10.1109/ACCESS.2021.3131386. 5 (available at ieeexplore.ieee.org/document/9628010). In cases where the present application conflicts with the document incorporated by reference, the present application controls. After act 206, this process concludes.” (pg. 7, lines 20-29 to pg. 8, lines 1-7)
Using available machine learning models is not improving the models.
Indeed, the August 2025 Memo explains that incorporation of an abstract idea into a practical application may be evidenced by claims that are limited to a particular solution. These are contrasted with claims that seek to cover every possible AI solution to achieve a desired outcome and are usually not incorporated into a practical application. Id. ("[a]n important consideration in determining whether a claim improves technology or a technical field is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome," emphasis added).
The claims though are using machine learning at a high level as a tool to perform an existing process of classifying messages into message categories. This is similar to sorting or screening email, which has been shown to be abstract (see MPEP 2106.04(a)(2) III C 2, where receive, screen, and distribute email was abstrarct).
Claims 1, 10, and 19 recite a computing system integrated into healthcare systems for facilitating "healthcare facility management" technology that "the inventors have identified significant disadvantages of the conventional ways in which patient portals handle textual messages from patients to physicians and other medical providers." See paragraph [0016] of the published application. The instant application also discloses that "FIG. 2 is a flow diagram showing a process performed by the facility in some embodiments to train a machine learning model to classify messages to service principals into message categories. In act 201, the facility defines a set of message categories that tend to occur among messages to service principals, and have similar preferred dispositions." See FIG. 2 and paragraph [0027] of the published application. In addition, the instant application discloses that "the facility improves the functioning of computer or other hardware, such as by reducing the dynamic display area, processing, storage, and/or data transmission resources needed to perform a certain task, thereby enabling the task to be permitted by less capable, capacious, and/or expensive hardware devices, and/or be performed with lesser latency, and/or preserving more of the conserved resources for use in performing other tasks.." See paragraph [0025] of the published application. Specifically, the pending claims recite a practical application that is used in conjunction with a particular machine that is integral to the claim and causes "healthcare facility management system" to present the information related to an healthcare plan so that the healthcare facility management system can make an important decision regarding the healthcare related plan. Accordingly, for at least the foregoing reasons, the pending claims are patent-eligible in accordance with the August 2025 Memo.
As such, for at least the aforementioned reasons, independent claims 1, 10, and 19 are directed toward patent-eligible subject matter under 35 U.S.C. §101. Accordingly, Applicant respectfully requests the reconsideration and withdrawal of the rejections of claims 1-7, 10, and 12-19 under 35 U.S.C. §101.
From the above cited memo (August 2025)…
“For example, the examiner should consider whether the technological limitations are being used as a tool to improve the recited judicial exception (e.g., automating a manual business process) or whether the claim as a whole provides an improvement to technology or a technical field.17 Claims that are determined to improve computer capabilities or improve technology or a technical field support a finding that the claim integrates the judicial exception into a practical application or amounts to significantly more than the judicial exception itself.” (page 5, para. 1)
Applicant’s specification teaches using available machine learning tools. There is no indication that technology itself is being improved. The rejection is modified but respectfully maintained based on the above response.
Applicant argues 35 USC §112 Rejection, pg. 12 of Remarks:
III. The Rejection Of The Claims Under 35 U.S.C. 112
Claims 1-7, 10, 12-19 were rejected under 35 U.S.C. § 112 as allegedly failing to comply with the written description requirement. See Office Action at page 15. More specifically, The Examiner stated that "claim 1 recites "in response to detecting that the machine learning classification model fails to determine the message category ... receiving additional user input indicating a new textual message category;" where "new textual message category" could not be found in the written description.
Claim 1, 10, and 19 have been amended to recite "new tted." Applicant respectfully submits that support for this amendment can be found, for example at paragraph [0041] of the published application as the Examiner indicated in Office Action at 16.
It is respectfully submitted that the foregoing amendment addresses the rejection, and therefore requests withdrawal of the rejection.
Withdrawn based on the claim amendment.
IV. The Rejection Of The Claims Under 35 U.S.C. 103
Claims 1, 3-5, 10,13-16, and 19 were rejected under 35 U.S.C. § 103 as allegedly being unpatentable over U.S. Patent Application Publication No. 2019/0108486 to Jain et al ("Jain") in view of US2003/0216928 to Shour et al ("Shour") in view of U.S. Patent No. US 1,1776,291 to Fleming et al ("Fleming"). Claims 2 and 12 were rejected under 35 U.S.C. § 103 as allegedly being unpatentable over Jain in view of Shour, Fleming, and US2022/0311728 to Sivaswamy ("Sivaswamy"). Claims 6 and 17 were rejected under 35 U.S.C. § 103 as allegedly being unpatentable over Jain in view of Shour, Fleming, and U.S. Patent No. US 10,811,139 to Wang ("Wang"). Claims 7 and 18 were rejected under 35 U.S.C. § 103 as allegedly being unpatentable over Jain in view of Shour, Fleming, in further view of U.S. Patent Application Publication No. US2021/0224818 to Choudhary ("Choudhary") and U.S. Patent Application Publication No. US2018/0285775 to Bergen ("Bergen").
Claims 1, 3-5, 10,13-16, and 19
For at least the following reasons, it is respectfully submitted that claim 1, as amended, is patentable over Jain in view of Shour, Fleming, Sivaswamy, and Bergen, taken alone or in combination with one another. As amended, claim 1 now recites in-part:
"A method of integratinga computing system into a healthcare system, comprising:
...subjecting the textual message to a machine learning classification model to determine a first message category of the textual message, wherein the machine learning classification model is trained to classify an input message into a plurality of message categoriescomprising at least two categories selected from the group consisting of: a prescription question, a test result question, anon-urgent medical question, and a personal note,
...processing the textual message corresponding to the textual message disposition strategy associated with the second message category selected,
wherein order of the processing the textual message comprises one or more order of acts selected from the group consisting of: rearranged, performed in parallel, omitted, included, divided and combined."
Thus, as recited by amended claim 1, referring to the exemplary embodiment shown below in FIG. 2, FIG. 11 and described in the specification, a method of integrating a computing system into a healthcare system now recites "a plurality of message categories comprising at least two categories selected from the group consisting of: a prescription question, a test result question, a non-urgent medical question, and a personal note" and "wherein order of the processing the textual message comprises one or more order of acts." At a minimum, Jain, Shour, Fleming, and Bergen fail to disclose or suggest the foregoing elements of claim 1.
Noted. However, sending questions to healthcare providers seems obvious. New art is cited that teaches this. Also, integrating a computing system into a healthcare system could not be found. Also, what does this mean?
Turning to the pending rejection and Jain, the Office has alleged that service provider system of Jain corresponds to the recited computing system. See Office Action at pages 28-29. Importantly, however, in contrast to the conclusion on which the pending rejection is based, Applicant respectfully submits that Jain does not disclose or suggest a plurality of message categories comprising at least two categories selected from the group consisting of: a prescription question, a test result question, anon-urgent medical question" nor "wherein order of the processing the textual message comprises one or more order of acts selected from the group consisting of: rearranged, performed in parallel, omitted, included, divided and combined" as recited in claim 1. Indeed, Jain is completely silent with respect to "one or more order of acts" as is recited in claim 1.
However, Jain teaches operations may be performed in parallel.
From Jain…
Parallel orders of operation processing…
“As illustrated in FIG. 4, an exemplary, numbered order of operations is provided, according to an embodiment. However, it should be noted that alternate orders of operation are also contemplated herein, e.g., parallel and/or serial orders, or any combination thereof) and the illustrated embodiment is not to be considered limiting.” [0081]
More specifically, Applicant respectfully submits that support for this amendment can be found throughout the instant application, for example paragraph [0029] of the published application states that "acts shown in FIG. 2 and in each of the flow diagrams discussed below may be altered in a variety of ways. For example, the order of the acts may be rearranged; some acts may be performed in parallel; shown acts may be omitted, or other acts may be included; a shown act may be divided into subacts, or multiple shown acts may be combined into a single act, etc." In contrast, Jain does not disclose or suggest one or more order (or sequence) of acts (or steps), let alone one or more order of acts comprising acts rearranged, performed in parallel, omitted, included, divided or combined. Thus, it is clear that one of skill in the art would not understand Jain to disclose or suggest a plurality of message categories comprising at least two categories selected from the group consisting of: a prescription question, a test result question, a non-urgent medical question, and a personal note" nor "wherein order of the processing the textual message comprises one or more order of acts selected from the group consisting of: rearranged, performed in parallel, omitted, included, divided and combined" as is recited in claim 1.
The Examiner acknowledges that "Jain ... teaches service provider" (such as email/communication service provider) and that Jain does "not teach timeframe and cost." See Office Action at page 28. Thus, as recited by amended claim 1, referring to the exemplary embodiment shown above in Fig. 5 and described in the specification, a method of integrating a computing system into a healthcare system now recites "receiving a textual message and an addressee service principal of the textual message via a first user interface element associated with a computing device integrated in the healthcare system." Thus, it is clear that one of skill in the art would not understand Jain to disclose or suggest the foregoing elements of claim 1.
Shour is relied on for timeframe and cost disclosing "suitable interface might be as simple as a browser or similar facility for providing web access. In other embodiments the interfaces may be provided using a client computer program downloaded onto the various computer systems, with suitable interface software for providing an interface to the overall system" in paragraph [0064] of Shour. See id at 29. However, Shour does not cure the foregoing deficiencies of Jain, nor is it relied upon as doing so. Indeed, Shour is also completely silent with respect to "a plurality of message categories comprising at least two categories selected from the group consisting of: a prescription question, a test result question, anon-urgent medical question, and a personal note" and an order of acts, "wherein order of the processing the textual message comprises one or more order of acts selected from the group consisting of: rearranged, performed in parallel, omitted, included, divided and combined" as is recited in claim 1.
The limitation is non-functional descriptive material and not given patentable weight. Arguably, Shour is not needed.
Fleming is relied on for timeframe and cost disclosing "ability to determine a threshold confidence value taught by Fleming." See id at 31. However, Fleming does not cure the foregoing deficiencies of Jain and Shour, nor is it relied upon as doing so. Indeed, Fleming is also completely silent with respect to "a plurality of message categories comprising at least two categories selected from the group consisting of: a prescription question, a test result question, anon-urgent medical question, and a personal note" and an order of acts, "wherein order of the processing the textual message comprises one or more order of acts selected from the group consisting of: rearranged, performed in parallel, omitted, included, divided and combined" as is recited in claim 1.
New prior art is cited to teach the amended claims.
Claims 2 and 12
Claims 2 and 12 were rejected under 35 U.S.C. § 103 as allegedly being unpatentable over Jain in view of Shour, Fleming, and US2022/0311728 to Sivaswamy ("Sivaswamy").
Sivaswamy is relied on for disclosing "sample messages" and "dependent and independent values." See id at 67. However, Sivaswamy does not cure the foregoing deficiencies of Jain, Shour, Fleming, nor is it relied upon as doing so. Indeed, Sivaswamy is also completely silent with respect to "a plurality of message categories comprising at least two categories selected from the group consisting of: a prescription question, a test result question, a non-urgent medical question, and a personal note" and an order of acts, "wherein order of the processing the textual message comprises one or more order of acts selected from the group consisting of: rearranged, performed in parallel, omitted, included, divided and combined" as is recited in claims 1 and 10, respectively.
Claims 6 and 17
Claims 6 and 17 were rejected under 35 U.S.C.§ 103 as allegedly being unpatentable over Jain in view of Shour, Fleming, and U.S. Patent No. US 10,811,139 to Wang ("Wang").
Wang is relied on for disclosing "reimbursable costs such as insurance claims." See id at 73-75. However, Wang does not cure the foregoing deficiencies of Jain in view of Shour, Fleming, nor is it relied upon as doing so. Indeed, Wang is also completely silent with respect to "a plurality of message categories comprising at least two categories selected from the group consisting of: a prescription question, a test result question, anon-urgent medical question, and a personal note" and an order of acts, "wherein order of the processing the textual message comprises one or more order of acts selected from the group consisting of: rearranged, performed in parallel, omitted, included, divided and combined" as is recited in claims 1 and 10, respectively.
New prior art is cited that teaches the claim amendments.
Claims 7 and 18
Claims 7 and 18 were rejected under 35 U.S.C. § 103 as allegedly being unpatentable over Jain in view of Shour, Fleming, in further view of U.S. Patent Application Publication No. US2021/0224818 to Choudhary ("Choudhary") and U.S. Patent Application Publication No. US2018/0285775 to Bergen ("Bergen").
Choudhary is relied on for disclosing "ability to determine poorly suited messages". See id at 76. However, Choudhary does not cure the foregoing deficiencies of Jain, Shour, Fleming, nor is it relied upon as doing so. Indeed, Choudhary is again completely silent with respect to "a plurality of message categories comprising at least two categories selected from the group consisting of: a prescription question, a test result question, anon-urgent medical question, and a personal note" and an order of acts, "wherein order of the processing the textual message comprises one or more order of acts selected from the group consisting of: rearranged, performed in parallel, omitted, included, divided and combined" as is recited in claims 1 and 10, respectively.
Bergen is relied on for disclosing "ability to discard a message using a link." See id at 77. However, Bergen does not cure the foregoing deficiencies of Jain, Shour, Fleming nor is it relied upon as doing so. Indeed, Bergen is again completely silent with respect to "a plurality of message categories comprising at least two categories selected from the group consisting of: a prescription question, a test result question, a non-urgent medical question, and a personal note" and an order of acts, "wherein order of the processing the textual message comprises one or more order of acts selected from the group consisting of: rearranged, performed in parallel, omitted, included, divided and combined" as is recited in claims 1 and 10, respectively.
New prior art is cited that teaches the claim amendments.
None of Choudhary and Bergen cures the deficiency of Jain, Shour, Fleming nor is relied upon as doing so. Similar to Jain, Shour, Fleming, Choudhary and Bergen are silent with respect to "a plurality of message categories comprising at least two categories selected from the group consisting of: a prescription question, a test result question, anon-urgent medical question, and a personal note" and an order of acts, "wherein order of the processing the textual message comprises one or more order of acts selected from the group consisting of: rearranged, performed in parallel, omitted, included, divided and combined" as is recited in claims 1 and 10, respectively.
Further, as independent claims 10, and 19 recite "a plurality of message categories comprising at least two categories selected from the group consisting of: a prescription question, a test result question, anon-urgent medical question, and a personal note" and "wherein order of the processing the textual message comprises one or more order of acts selected from the group consisting of: rearranged, performed in parallel, omitted, included, divided and combined" corresponding to those of claim 1 discussed above, it is respectfully submitted that claims 10, and 19 are also patentable over Jain in view of Shour, Fleming, Sivaswamy, Choudhary, and Bergen, for at least the same reasons as claim 1.
Accordingly, as each and every element of the claim must be disclosed or suggested by the cited prior art references in order to establish aprimafacie case of obviousness (see, M.P.E.P. § 2143.03), and the cited prior art references fail to do so for at least the foregoing reasons, it is clear that claims 1, 10, and 19 are patentable over the cited prior art references.
For all of the foregoing reasons, it is respectfully submitted that the pending independent claims are patentable over the cited prior art references.
New prior art is cited that teaches the claim amendments.
IV. Dependent Claims
Under Federal Circuit guidelines, a dependent claim is nonobvious if the independent claim upon which it depends is allowable because all the limitations of the independent claim are contained in the dependent claims, Hartness International Inc. v. Simplimatic Engineering Co., 819 F.2d at 1100, 1108 (Fed. Cir. 1987). Accordingly, as the pending independent claims are patentable for at least the reasons set forth above, it is respectfully submitted that all dependent claims 2-7 and 12-18 thereon are also patentable.
New prior art is cited that teaches the claim amendments. Based on the above response, the rejection is respectfully modified for the claim amendments but maintained.
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 1-7, 10, and 12-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-7, 10, and 12-19 are directed to a method, product, or system, which are statutory categories of invention. (Step 1: YES).
The Examiner has identified method Claim 1 as the claim that represents the claimed invention for analysis and is similar to product claim 10.
Claim 1 recites the limitations of:
A method in a computing system, comprising:
receiving a textual message and an addressee service principal of the textual message via a first user interface element associated with a computing device integrated in the healthcare system;
displaying, via a second user interface element associated with the computing device, at least information relating to a timeframe and a cost notification by the addressee service principal for responding to the textual message;
subjecting the textual message to a machine learning classification model to determine a first message category of the textual message, wherein the machine learning classification model is trained to classify an input message into a plurality of message categories comprising at least two categories selected from the group consisting of: a prescription question, a test result question, a non-urgent medical questions, and a personal note, each of the plurality of message categories having a textual message disposition strategy;
performing the textual message disposition strategy corresponding to the message category determined for the textual message;
in response to detecting that the machine learning classification model fails to determine the first message category of the textual message with a confidence level exceeding a selected threshold value, displaying, via a third user interface element associated with the computing device, at least a portion of the plurality of message categories and receiving additional user input indicating a second message category selected; and
processing the textual message corresponding to the textual message disposition strategy associated with the second message category selected, wherein order of the processing the textual message comprises one or more order of acts selected from the group consisting of: rearranged, performed in parallel, omitted, included, divided and combined.
These above limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity. The claim recites elements, highlighted in bold above, which covers performance of the limitation as managing interactions between people (e.g., receiving user input message for an addressee service principal, displaying information relating to a timeframe and cost notification by the addressee service principal for responding to the textual message). The performing the textual message disposition strategy corresponding to the message category determined for the textual message and processing the textual message corresponding to the textual message disposition strategy is based on the textual message that includes an addressee service principal, therefore, performing the disposition strategy and processing the textual message is based on interaction between the user and address service principal. Displaying at least a portion of the plurality of message categories and receiving additional user input indicating a new textual message category is following rules and instructions, therefore, managing personal behavior. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as managing interactions between people or managing personal behavior, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. See also MPEP 2106.04(a)(2) II where it is the activity and not the number of people that determines if the claim falls in this category (a user sending a message and disposition of the message is interaction between people). Accordingly, the claim recites an abstract idea. Claims 10 and 19 are also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims are abstract)
The above claim also recites steps that can be performed in the mind of a person or with pen and paper, the claims are also abstract under Mental Process grouping of abstract ideas. See also MPEP 2106.04(a)(2) III C, where using a computer to perform a judicial exception has been shown to be abstract. A person in their mind or with pen and paper can perform a textual message strategy and detect a model fails to determine a message category with a confidence level and perform a disposition strategy. Further, receive, screen and distribute an email on a computer network has been shown to be a mental processes (MPEP 2106.04(a)(2) III C 2 and 2106.04(a)(2) III B), where the instant claims receive textual message, determine a message category, perform textual message disposition strategy, and processing the textural message corresponding to the textual message disposition strategy. Claims 10 and 19 are also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims are abstract)
This judicial exception is not integrated into a practical application. In particular, the claims only recite: a first user interface element, second user interface element, computing device, machine learning, third user interface element (Claim 1); computer-readable media, computing system, first user interface element, second user interface element, machine learning, third user interface element (Claim 10); communication network, computing device, non-transitory computer readable storage medium, first user interface element, second user interface element, third user interface element (Claim 19). The computer hardware is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. The machine is a generic machine. The machine learning is recited and applied at a high level of generality. The machine learning model is trained to classify an input message into a plurality of message categories is recited at a high level of generality. See also Applicant’s specification (pg. 7, lines 20-29 to pg. 8, lines 1-7) where available machine learning models are being used. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore claims 1, 8, and 10 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer hardware amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See Applicant’s specification, pg. 6, lines 3-26 teach various computing systems that can be used and MPEP 2106.05(f) where applying a computer as a tool is not indicative of significantly more. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Steps such as receiving and storing are steps that are considered insignificant extra solution activity and mere instructions to apply the exception using general computer components (see MPEP 2106.05(d), II). Thus claims 1, 8 and 10 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claims 2-7 and 12-18 further define the abstract idea that is present in their respective independent claims 1 and 10 and thus correspond to Certain Methods of Organizing Human Activity and Mental Processes and hence are abstract for the reasons presented above. The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the claims 2-7 and 12-18 are directed to an abstract idea. Thus, the claims 1-7, 10, 12-19 are not patent-eligible.
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 1-7, 10, and 12-19 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.
Claim 1 recites “receiving a textual message… a first user interface element associated with a computing device integrated in the healthcare system” where no teaching of computing device integrated in the healthcare system can be found.
From Applicant’s specification on “”healthcare”…
“People increasingly use patient portals—such as via the web or smartphone apps—to engage electronically with their healthcare. It is common for patient portals to permit their users to send textual messages to their physicians and other medical providers.” [0001]
The above teaches use patient portals via web or smartphone to engage electronically with their healthcare. There is no teaching of a computing device integrated in the healthcare system. There is no teaching of “integrated” and “healthcare system.” Claims 10 and 19 have a similar problem.
Dependent claims 2-7 and 12-18 are further rejected as they depend from their respective independent claim.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1-7, 10, and 12-19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites “receiving a textual message… a first user interface element associated with a computing device integrated in the healthcare system” where it is indefinite as to “integrated” and “healthcare system” as neither is taught and these could be anything. For examination purposes, this is interpreted as a computing device can somehow access a computer for healthcare information. Claims 10 and 19 have a similar problem.
Dependent claims 2-7 and 12-18 are further rejected as they depend from their respective independent claim.
Examiner Request
The Applicant is requested to indicate where in the specification there is support for amendments to claims should Applicant amend. The purpose of this is to reduce potential 35 U.S.C. §112(a) or §112 1st paragraph issues that can arise when claims are amended without support in the specification. The Examiner thanks the Applicant in advance.
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 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, 10,13-16, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Pub. No. US 2019/0108486 to Jain et al. in view of Pub. No. US 2006/0184393 to Ewin et al. Pub. No. US 2003/0216928 to Shour in view of Patent No. US 11776291 to Fleming et al.
Claim 1 recites the limitations of:
A method in a computing system, comprising:
receiving a textual message and an addressee service principal of the textual message via a first user interface element associated with a computing device integrated in the healthcare system;
{
From Applicant’s specification on “element”…
“While Figure 5 and each of the display diagrams discussed below show a display whose formatting, organization, informational density, etc., is best suited to certain types of display devices, those skilled in the art will appreciate that actual displays presented by the facility may differ from those shown, in that they may be optimized for particular other display devices, or have shown visual elements omitted, visual elements not shown included, visual elements reorganized, reformatted, revisualized, or shown at different levels of magnification, etc.” (pg. 10, lines 10-16)
Therefore, an element from Fig. 5 appears to be something (text, icon, etc.) that is displayed.
}
Jain et al. teaches:
Providing (receiving) support request (textual message) addressed to single support account or entire support team (addressee of service principal)…
“…As referred to herein, a “sender” may be any type of user or automated mechanism for providing support requests and/or information related thereto. Often times, the systems and services may receive large numbers, e.g., hundreds, thousands, or tens of thousands, of support requests from senders. When senders are not able to determine a specific owner/recipient for their support request, e.g., emails may be addressed to a single support email account for all services/products rather than a specific team or may be addressed to an entire support team instead of a specific feature owner(s) within the team, mis-routing or slow routing of support requests can occur which increases TTE (e.g., Time-to-Engage) and TTR (e.g., Time-to-Resolve) and can negatively impact the user. When the correct owner (e.g., a recipient) for a request does not receive notice of the request quickly, the TTE increases—that is, the time-to-engage the request after its submission and begin resolution by the correct support group is negatively impacted by mis-routings of request to incorrect recipients such as owners/support groups…” [0031]
Computing devices with GUIs (graphical user interfaces) to receive requests…
“Remote device 102a and remote device 102b may be any type of computing device or computing system, including a terminal, a personal computer, a laptop computer, a tablet device, a smart phone, etc., that may be used to provide support requests, e.g., via communication client 112a and/or communication client 112b, in which a sender includes support request information. For instance, as shown in FIG. 1, remote device 102a includes communication client 112a, and remote device 102b includes communication client 112b. In embodiments, remote device 102a and remote device 102b are configured to respectively activate communication client 112a and/or communication client 112b to enable a user to provide information in a support request that is used to perform intelligent and automatic handling thereof. In some embodiments, remote device 102a and remote device 102b are configured to respectively receive interfaces such as GUIs from host server 104 to enable a user to provide information in a support request that is used to perform intelligent and automatic handling thereof. That is, communication client 112a and/or communication client 112b may operate independently of host server 104. In embodiments, remote device 102a and/or remote device 102b may include a stored instance of a communication client, as described above, which may be received from host server 104. In embodiments, communication client 112a and/or communication client 112b may be any type of electronic communication client or electronic communication application, such as email clients, messaging applications, portals, and/or the like.” [0040]
See First, Second, and Third Interfaces below.
See Integrated Healthcare below.
displaying, via a second user interface element associated with the computing device, at least information relating to a timeframe and a cost notification by the addressee service principal for responding to the textual message;
{
From Applicant’s specification…
“Figure 6 is a display diagram showing sample contents of a display presented by the facility in some embodiments in response to the user activating messaging control 513 for a particular service principal. The display 600 presents 20 information 601 establishing expectations for the message that is to be sent, including the amount of time it can take to receive a response, and the fact that sending the message may result in being charged. The display includes a next control 602 that the user can activate to proceed to prepare the message.” (pg. 10, lines 17-23)
Therefore, a message with time it takes to respond and amount may be charged.
}
[No Patentable Weight is given to non-functional descriptive claim language of “at least information relating to a timeframe and a cost notification by the addressee service principal for responding to the textual message;” as this is just displaying information without functional use.]
Requests such as for billing (cost information) and support…
“The techniques and embodiments described herein provide for intelligently and automatically supporting electronic communication requests (also “requests” or “support requests” herein), such as but not limited to, electronically mailed (“emailed”) support requests, technical support requests, postings on messaging threads or forums such as those hosted by websites, social media postings, instant messages, conversations with automated mechanisms such as “bots,” billing, feedback, notifications, etc., that include requests such as for support, information, user access, and/or the like…” [0031]
Second electronic communication (second user interface) with response…
“In step 310, a second electronic communication is generated that includes the second information and the second electronic communication is provided to at least one of the sender or the recipient, and/or the first electronic communication is provided to the recipient. For example, responder 416 of FIG. 4 may be configured to generate an electronic communication for reply to the sender of the support request that includes technical support information as determined in step 308 by featurizer/selector 408. In embodiments, the electronic communication for reply to the sender may also be provided to the determined recipient for support assistance/resolution. Responder 416 may also be configured to provide the support request to the determined recipient, e.g., in cases of first impression for technical/support issues in which similar issues have never before been provided in electronic communications. Second electronic communications may also be personalized for the sender, as described herein.” [0087]
See Timeframe and Cost below.
subjecting the textual message to a machine learning classification model to determine a message category of the textual message, wherein the machine learning classification model is trained to classify an input message into a plurality of message categories comprising at least two categories selected from the group consisting of: a prescription question, a test result question, a non-urgent medical questions, and a personal note, each of the plurality of message categories having a textual message disposition strategy;
Where request information (message) lacks information to correctly identify owner/team (therefore, before sending textual message to appropriate party), machine learning consumes vector information…
“The embodiments described herein provide for several techniques for properly and automatically routing support requests, and responding to support requests with technical support information, in an intelligent manner. Such techniques allow for scaling to large numbers of services, handling unstructured user inputs, and making accurate routing decisions based on limited information. For instance, to scale to large numbers of services, a communication support system may be configured to provide tracking workflows for hundreds to thousands of services where each service in turn may have several associated support teams or groups. In embodiments, for users or automated mechanisms creating and providing support requests via communication clients, the communication support system is configured to overcome difficulties in providing support requests to correct owners/recipients for support of features/products/systems/services. Because there may not be enough support request information available to manually identify the correct issue owner/team by the sender (e.g., a user notes specific system or service performance feature problems, but the underlying root cause could have been a problem in network, storage, other broad sets of services, etc.), the described techniques and embodiments provide an architecture configured to automatically accomplish such a task based on machine-learning algorithms that consume feature vectors for provided information.” [0034]
Where feature vectors are classified using classification models…
“Models/algorithms, such as classification models/algorithms, may be trained offline for deployment and utilization as described herein, according to one or more featurization operations described herein for structuring input data and determining feature vectors, and model trainer 804 may be configured to train models/algorithms using described machine learning techniques, according to embodiments. The techniques and embodiments herein may also operate according to one or more machine learning models/algorithms, such as, but without limitation, ones of the MicrosoftML machine learning models/algorithms package, Microsoft® Azure® machine learning models/algorithms, etc., from Microsoft Corporation of Redmond, Wash.: [0130]
Where remote device to provide support requests (textual message) may be portals…
“Remote device 102a and remote device 102b may be any type of computing device or computing system, including a terminal, a personal computer, a laptop computer, a tablet device, a smart phone, etc., that may be used to provide support requests, e.g., via communication client 112a and/or communication client 112b, in which a sender includes support request information. For instance, as shown in FIG. 1, remote device 102a includes communication client 112a, and remote device 102b includes communication client 112b. In embodiments, remote device 102a and remote device 102b are configured to respectively activate communication client 112a and/or communication client 112b to enable a user to provide information in a support request that is used to perform intelligent and automatic handling thereof. In some embodiments, remote device 102a and remote device 102b are configured to respectively receive interfaces such as GUIs from host server 104 to enable a user to provide information in a support request that is used to perform intelligent and automatic handling thereof. That is, communication client 112a and/or communication client 112b may operate independently of host server 104. In embodiments, remote device 102a and/or remote device 102b may include a stored instance of a communication client, as described above, which may be received from host server 104. In embodiments, communication client 112a and/or communication client 112b may be any type of electronic communication client or electronic communication application, such as email clients, messaging applications, portals, and/or the like.” [0040]
Example of classification trained algorithms for determining (predicting) routing requests…
“Classification models/algorithms may be trained, offline in some embodiments, for deployment, according to one or more featurization operations used by communication supporter 208 for structuring input data, and model trainer 220 may be configured to train models using machine learning techniques and instance weighting, according to embodiments. In embodiments, classification models may be or may comprise algorithms, such as machine-learning algorithms, for automatically and intelligently determining recipients for routing electronic communication support requests. Further details concerning model training are provided below.” [0049]
Relevant support information provided to user, and provide prior communication information to the sender (therefore, before user activates a user interface element) and provide routing support requests to correct feature owner recipients based on algorithm/model outputs…
“Methods for automatic and intelligent electronic communication support, including using machine learning, are performed by systems and apparatuses. The methods intelligently and automatically route electronic communication support requests and intelligently and automatically provide senders with information related to their support requests. The methods generate feature vectors from cleaned request information via featurization techniques, and utilize machine-learning algorithms/models and algorithm/model outputs based on the input feature vectors. Based on the algorithm/model outputs and personalized to the specific sender, relevant support information is automatically provided to the sender. The methods also determine a set of prior communications related to the support request based on a similarity measure, and provide prior communication information to the sender. The methods also include routing support requests to correct feature owner recipients based on the algorithm/model outputs.” [0003] Inherent with routing to correct owner is not sending the communication information before it subjects the document to machine learning model to predict category to which document belongs.
Monitor electronic messages including listen for and received by server new support requests, therefore, sending message not necessary…
“Notifier 404 is configured to monitor electronic messages received at server 402, such as support request communications (e.g., emails). In embodiments, notifier 404 may include or utilize functionality of an API for an exchange web service, e.g., StreamingNotification offered by Microsoft Corporation of Redmond, Wash., to listen for and receive new support requests from server 402. When a new support request is received by server 402, notifier 404 is configured to store the received request in DB 406 for later use/reference, and to alert and provide the received support request to featurizer/selector 408. In embodiments, notifier 404 may be included as a component of system 200 of FIG. 2, e.g., as part of communication supporter 208.” [0074]
Responder provide responses to senders based on recipient predictions…
“Responder 416 may be configured to perform the functions and operations of responder 230 of FIG. 2. For example, responder 416 may be configured to automatically generate electronic messages that respond to received support requests and/or to provide received support requests to recipients. In embodiments, responder 230 may provide received support requests via transmitter 418 to recipients in support groups based on recipient predictions of featurizer/selector 408 (e.g., a machine-learning classifier). Responder 416 may be configured to generate and provide responses to support requests (as responsive electronic communications) to senders and/or recipients in support groups based on recipient predictions of selector 212. These generated responses may include automatically selected technical support information obtained by featurizer/selector 408 from DB 406 and/or previously-received support requests (or communication threads associated with the previously-received support requests) obtained by featurizer/selector 408 via locator 412.” [0079]
Training information into categories…
“Referring again to flow diagram 600 of FIG. 6, training portion 602 of flow diagram 600 begins with the receipt of training data/testing data 606 (“data 606”). Data 606 may comprise previously-received support requests and/or resolutions, senders and recipients thereof, as well as communication threads thereof, a priori information, tailored support requests, etc., divided into known categories/taxonomies corresponding to self-help content for previously-identified problems/issues (e.g., “training data”). Data 606, or a portion thereof, may also be tagged with class labels as “testing data” or “training data” for modeling purposes. In embodiments, when prior support requests and response communications for resolution are identified for new support requests received, as described herein, the start and end indices for answer strings in the prior responses may be annotated as training data. Data 606 may be provided to a cleaner 608.” [0121]
Reduced network routing based on intelligently and automatically determining recipients, therefore, before the message is sent…
“The embodiments and techniques described herein provide improved performance of computing devices and operations executing thereon. By one or more of the techniques and embodiments described, recipients are predicted and technical support information is obtained intelligent and automatic electronic communication support, including using machine-learning, e.g., for support requests, in ways that reduce usage for system resources and also improve system operations. For instance, as noted above, the number of possible recipients for support requests may vary greatly from a relatively small number to thousands of support groups, staff members, and/or engineers. The recipients, according the techniques and embodiments herein, are intelligently and automatically predicted based on an incoming support request and stored support request communication threads. By intelligently and automatically determining recipients for the routing of and responding to support requests, load due to mis-routings is significantly reduced for the network utilized by technical support groups and the associated recipients. Additionally, TTE and TTR are reduced thereby improving productivity and operations of features/products/systems/services for which support requests are provided by senders. That is, issues for features/products/systems/services accessed by senders may be timely mitigated thus increasing both features/products/system/service operational efficiency as well as operational quality.” [0158]
See Questions below.
performing the textual message disposition strategy corresponding to the message category determined for the textual message;
{
Applicant’s specification does not teach “message disposition strategy.
From Applicant’s original Claim 1 (filed 09/22/2022)…
“performing the textual message disposition strategy to which the accessed mapping maps the determined textual message category with respect to the defined textual message.”
Therefore, the disposition strategy maps the message category tot the text.
}
Support requests (text messages) mapped to services (categories)…
“In step 306, the feature vector is provided as an input to a machine-learning model that automatically determines a model output based on the feature vector. For example, ML model 410 may generate this model output through a classification of the support request, based on the feature vector, into one or more predefined taxonomies determined during the training of model 410. That is, support requests may be mapped to features/products/systems/services by way of ML model 410. In embodiments, featurizer/selector 408 of FIG. 4 may be configured to automatically determine a recipient(s) based on the feature vector generated in step 304. Featurizer/selector 408 may be configured to process the feature vector according to an algorithm or model to generate an output for predicting the correct recipient(s) for the support request. According to embodiments, featurizer/selector 408 may utilize ML model 410 in making the prediction. ML model 410 may be a classifier, in embodiments, such as a machine-learning classifier that utilizes machine learning techniques based on a learning model or classification model. ML model 410 is configured to provide its output (e.g., a correct recipient prediction) to featurizer/selector 408.” [0085]
Support requests provided to categories of support requests…
“In embodiments, feature/product/service/system owners and/or teams, e.g., recipients of support requests, may be notified about tasks/work items in their respective areas of support provision. For example, tasks/work items may include support requests as described herein. The techniques and embodiments described provide for a digest summary (e.g., updated hourly, daily, or otherwise) of support request that may be provided to the owning teams/engineers for each of the categories of support requests.” [0154]
in response to detecting that the machine learning classification model fails to determine the first message category of the textual message with a confidence level exceeding a selected threshold value, displaying, via a third user interface element associated with the computing device, at least a portion of the plurality of message categories and receiving additional user input indicating a second message category selected; and
{
From Applicant’s specification on threshold and additional input…
“FIG. 4 is a flow diagram showing a process performed by the facility in some embodiments to process a draft message prepared by a user that is initially addressed to a service principal. In act 401, the facility receives the draft message and its original addressee. For example, in some embodiments, the user types this information into a web page or application form provided by the facility, and/or an electronic medical record (“EMR”) program. In act 402, the facility applies the trained machine learning model to the draft message received in act 401 to predict the category of the draft message. In some embodiments, the model returns a confidence level for each of the message categories indicating the likelihood that the message is a member of that category. In some embodiments, the facility selects as the category of a message any categories having a confidence level higher than a particular threshold, such as 90%. In some embodiments, the facility selects as the category of the message the category having the highest confidence level. In act 403, the facility applies to the draft message the disposition specified for the category predicted in act 402, such as the disposition identified by the category disposition table shown in FIG. 3. After act 403, this process concludes.” [0032]
“FIG. 11 is a display diagram showing sample contents of a display presented by the facility in some embodiments to permit a user to select the appropriate category for a message if predicting the message category automatically is unsuccessful, such as where it produces no category predictions with an adequate confidence level. The display 1100 includes a third message 1110; a prompt 1120 to select message category; and available categories 1130 from among which the user can choose.” [0041]
The above teaches select appropriate category, if predicting category is unsuccessful, or no category with an adequate confidence level.
}
Model updated based on additional (second) requests (input)…
“According to embodiments, model 222 may comprise one or more models or templates, as described herein, and may be stored by memory 206. Model 222 may be incrementally, or wholly, updated by model trainer 220 based on feedback, additional electronic communication support requests received, and/or the like.” [0060]
Mis or incomplete routings for support requests…
“Reporter 216 may be configured to provide re-route indications of mis- and/or incomplete-routings for support requests to recipients and/or to model training components, such as model trainer 220, and/or as described in detail below (e.g., an evaluator as described in FIG. 6). Reporter 216 may also be configured to determine and provide metrics related to a support request to model trainer 220. Reporter-determined metrics for a support request may include TTE, TTR, a number of mis-routings, portions of sender and/or recipient feedback, support request information, and/or the like.” [0066]
Solicit feedback from senders with text and options (second text message)…
“Responder 230 may also be configured to solicit feedback from senders through generated responses to support requests. Solicitations may be made by text and/or selectable options that would indicate the feedback of the sender with respect to resolution and technical support information provided for a support request.” [0069]
See Fail below.
processing the textual message corresponding to the textual message disposition strategy associated with the second message category selected, wherein order of the processing the textual message comprises one or more order of acts selected from the group consisting of: rearranged, performed in parallel, omitted, included, divided and combined.
{
Applicant’s specification does not teach “message disposition strategy.”
From Applicant’s original Claim 1 (filed 09/22/2022)…
“performing the textual message disposition strategy to which the accessed mapping maps the determined textual message category with respect to the defined textual message.”
Therefore, the disposition strategy maps the message category to the text.
Applicant’s specification on order of processing textual messages…
“Those skilled in the art will appreciate that the acts shown in FIG. 2 and in each of the flow diagrams discussed below may be altered in a variety of ways. For example, the order of the acts may be rearranged; some acts may be performed in parallel; shown acts may be omitted, or other acts may be included; a shown act may be divided into subacts, or multiple shown acts may be combined into a single act, etc.” [0029]
Therefore, any of the steps such as training reads on the limitations.
}
Support requests (text messages) mapped to services (categories)…
“In step 306, the feature vector is provided as an input to a machine-learning model that automatically determines a model output based on the feature vector. For example, ML model 410 may generate this model output through a classification of the support request, based on the feature vector, into one or more predefined taxonomies determined during the training of model 410. That is, support requests may be mapped to features/products/systems/services by way of ML model 410. In embodiments, featurizer/selector 408 of FIG. 4 may be configured to automatically determine a recipient(s) based on the feature vector generated in step 304. Featurizer/selector 408 may be configured to process the feature vector according to an algorithm or model to generate an output for predicting the correct recipient(s) for the support request. According to embodiments, featurizer/selector 408 may utilize ML model 410 in making the prediction. ML model 410 may be a classifier, in embodiments, such as a machine-learning classifier that utilizes machine learning techniques based on a learning model or classification model. ML model 410 is configured to provide its output (e.g., a correct recipient prediction) to featurizer/selector 408.” [0085]
Support requests provided to categories of support requests…
“In embodiments, feature/product/service/system owners and/or teams, e.g., recipients of support requests, may be notified about tasks/work items in their respective areas of support provision. For example, tasks/work items may include support requests as described herein. The techniques and embodiments described provide for a digest summary (e.g., updated hourly, daily, or otherwise) of support request that may be provided to the owning teams/engineers for each of the categories of support requests.” [0154]
Training information into categories…
“Referring again to flow diagram 600 of FIG. 6, training portion 602 of flow diagram 600 begins with the receipt of training data/testing data 606 (“data 606”). Data 606 may comprise previously-received support requests and/or resolutions, senders and recipients thereof, as well as communication threads thereof, a priori information, tailored support requests, etc., divided into known categories/taxonomies corresponding to self-help content for previously-identified problems/issues (e.g., “training data”). Data 606, or a portion thereof, may also be tagged with class labels as “testing data” or “training data” for modeling purposes. In embodiments, when prior support requests and response communications for resolution are identified for new support requests received, as described herein, the start and end indices for answer strings in the prior responses may be annotated as training data. Data 606 may be provided to a cleaner 608.” [0121]
Parallel orders of operation processing…
“As illustrated in FIG. 4, an exemplary, numbered order of operations is provided, according to an embodiment. However, it should be noted that alternate orders of operation are also contemplated herein, e.g., parallel and/or serial orders, or any combination thereof) and the illustrated embodiment is not to be considered limiting.” [0081]
Integrated Healthcare
Jain et al. teaches service provider. They do not teach computer device integrated with healthcare system.
Ewin et al. also in the business of service provider teaches:
“The medical evaluation form can be communicated to a doctor via a network, such as the Internet. For example, the medical evaluation form can be communicated to a doctor via email. Alternatively, the medical evaluation form can be communicated to a doctor via facsimile.” [0024]
Server with client (integrated healthcare system)…
“Referring now to FIG. 4, a server 41 is in communication with the Internet 42 and a client 43 is also in communication with the Internet 42. Server 41 and client 43 are configured so as to facilitate filling out a medical evaluation form online and facilitate reviewing the medical information form online. Server 41 can be in either wired (as shown) or wireless communication with the Internet 42. Similarly, client 43 can be in either wired or wireless (as shown) communication with the Internet 42.” [0091]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of Jain et al. the ability to use an integrated system as taught by Ewin et al. 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. Further motivation is provided by Ewinl et al. who teaches the benefits such a system for patients asking healthcare questions.
Timeframe and Cost
The combined references teach service provider. They do not teach timeframe and cost.
Shour also in the business of service provider teaches:
“In embodiments methods and systems are provided for facilitating knowledge exchange. The methods and systems may include establishing a process for allowing a service requester to request a knowledge-based service from a service provider; establishing a prerequisite data set desired by the service provider for assisting the service provider in determining whether to accept a request for service; and routing a service request to a service provider based on completed data sets of a service requester. The service may be provided online, or through a combination of online and offline steps (e.g., store and forward or store and retrieve models). References to online services throughout should be understood to encompass completely online services, as well as services that combine online with offline steps.” [0023]
Response time to service requests and fees (costs)…
“When all data requirements are entered, the professional 1002 proceeds to the step 1028, where the professional 1002 defines the response time that the professional 1002 expects to achieve in meeting service requests. For example, an engineer professional 1002 might indicate that she will respond to queries within 48 hours, or for non-emergency situations within two or more weeks. The administrator for a given enterprise or sub-enterprise can later use these response times to help monitor performance and send alerts as response times are approached without appropriate action. Next, at a step 1030, the professional 1002 defines fees for the services that the professional 1002 will provide, such as hourly fees, transaction-based fees, contingency fees, time and materials fees, and other fees.” [0078]
Example of interfaces for display…
“Referring to FIG. 7, a schematic diagram shows high-level system components for a computer system 700 to support an intelligent knowledge exchange market. The computer system 700 includes system elements for various elements that participate in the marketplace. Thus, there is a host system 702 that facilitates performance of the various functions of the host 2002 of the marketplace. There is also a plurality of enterprise systems 704 that facilitate functions of the enterprises 2004 that participate in the marketplace. Similarly, there are a plurality of office systems 708 to facilitate functions of offices 2008 and a plurality of professional systems 710 that facilitate functions of professionals 2010. These systems may be connected by a communications facility, such as a network 712, which in an embodiment is the Internet, but which in other embodiments might be a portion of the Internet, such as the worldwide web, or a wide area network, local area network, wireless network, intranet, dedicated line, or other network or communication facility for allowing connection between the various entities. In embodiments, the system for the marketplace is a web-based system, with each of the host system 702, enterprise system 704, professional system 710, and office system 708 comprising not separate systems, but rather interfaces for interaction with the central web-based system. A suitable interface might be as simple as a browser or similar facility for providing web access. In other embodiments the interfaces may be provided using a client computer program downloaded onto the various computer systems, with suitable interface software for providing an interface to the overall system. In embodiments the software may be served to the various entities by an ASP model or similar facility.” [0064]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to provide a timeframe and cost as taught by Shour 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. Further motivation is provided by Shour who teaches the financial benefit of determining both time and cost for providing a service to a client.
Questions
The combined references teach questions. They do not teach specific questions.
Ewin et al. also in the business of questions teaches:
Patient with questions for doctor (personal note) or drug interactions (prescription question), need for a follow-up visit (non-urgent medical questions)…
“Medical questions can be answered online. The medical questions can comprise questions either from the patient or from medical personnel. For example, a patient may have questions for the doctor about the seriousness of a condition, treatment options, drug interactions, contraindications, or side-affects, and/or the need for a follow-up visit. A doctor may have questions for the patient regarding the effectiveness of treatment, the presence of drug side-effects, and/or the progression of an illness.” [0036]
Request (question) prescription…
“Prescriptions can be requested and/or filled online. Prescriptions refills can be requested online. For example, a request for a refill can be sent to a doctor's office for authorization, if necessary, and then forwarded to a pharmacy where it is filled.” [0037]
Email for interaction…
“According to one or more embodiments, the present invention facilitates the ability for an online health care provider and patient interaction that becomes a medical evaluation, with the judgment of the practitioner having a much more sound basis since a more thorough history is available according to the present invention than what occurs according to contemporary practice via a phone, email or other non-face-to-face evaluation.” [0092]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability for patients to ask healthcare questions as taught by Ewin et al. 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. Further motivation is provided by Ewin et al. who teaches the benefits to patients of asking healthcare questions.
Fail
The combined references teach machine learning. They do not teach fail.
Fleming et al. also in the business of classification teaches;
Threshold confidence value not satisfied (fail) for classification model and display request additional user input…
“Once documents are labeled, such as via user input as described above, one or more processes may be performed to predict the classification of other documents in a document set. For example, the system may receive user input data indicating in class documents and out of class documents from a subset of first documents. For example, if the first documents include 1,000 documents, the user input data may indicating classification for a subset, such as 20, of those documents. The system may then utilize that user input data to train a classification model, such that the classification model is configured to determine whether a given document is more similar to those documents marked in class or more similar to those documents marked out of class. Utilizing the classification model, as trained, the system may predict the classification of the remainder of the first documents that were not labeled by the user input. Each or some of the predictions for the remainder of the documents may be associated with a confidence value indicating how confident the system is that the classification model accurately determined the classification of a given document. A threshold confidence value may be determined and the system may determine whether an overall confidence value associated with the classification model satisfies that threshold confidence value. In instances where the confidence value does not satisfy the threshold confidence value, the system may cause an indication of this determination to be displayed and may request additional user input data for retraining the classification model. In instances where the confidence value satisfies the threshold confidence value, the system may receive second documents for classification prediction. The second documents may be received based at least in part on a user uploading additional documents and/or from the system retrieving additional documents from one or more databases. The classification model may then be utilized to predict classification of this second document set.” (col. 8, lines 13-47)
A user interface with categories that a user may select for training purposes…
“In addition to the techniques for training the classification models described above, the classification models may also be trained and/or organized based at least in part on classifications of the documents. For example, when the documents are patents and patent applications, a predetermined classification system may be established for classifying the subject matter of a given document. The classification system may be determined by the platform, by one or more users, and/or by a third party. For example, patents and patent application may be associated with a predefined classification system such as the Cooperative Patent Classification (CPC) system. The CPC system employs CPC codes that correspond to differing subject matter, as described in more detail herein. The CPC codes for a given document may be identified and the categories associated with those codes may be determined. A user interface may be presented to the user that presents the determined categories and allows a user to select which categories the user finds in class for a given purpose. The selected categories may be utilized as a feature for training the classification models. Additionally, or alternatively, the platform may determine the CPC codes for documents marked as in class and may train the classification models to compare those CPC codes with the CPC codes associated with the documents to be analyzed to determine classification.” (col. 9, lines 41-65)
Where user selects documents class and confidence values based on the class…
“… Once a model is selected, the user may start providing indications of which documents 102 are in class and which documents 102 are out of class. These indications may be utilized to train the selected model. However, depending on the amount and quality of the user indications, the output of the trained model may not be associated with a high confidence value. In these examples, the status may indicate that the model has been trained but is not yet stable. Once the confidence values increase as the model is retrained, the status may indicate that the model is stable.” (col. 12, lines 6-15)
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to determine a threshold confidence value as taught by Fleming et al. 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. Further motivation is provided by Fleming who teaches the benefits of determining a confidence value in order to determine if further information is needed for classification purposes. It’s also useful when training machine learning models as this improves the predictiveness of output of such models.
First, Second, and Third Interfaces
The combined references teach display and interfaces. They do not explicitly teach first, second, and third interface elements. However one of ordinary skill in the art would recognize that the interaction between a user and service provider would provide various interface elements.
It would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s filing to modify the combined references with the knowledge available to such an artisan that first, second, and third interfaces could be used when for interactions between a user and a service provider. This would have been known work in the field of endeavor prompting variations of it in the same field based on use of providing information on a display and would provide predictable results.
Regarding claim 3
The method of claim 1 wherein performing the textual message disposition strategy comprises causing the defined textual message to be delivered to the addressee service principal with information identifying the determined textual message category.
Jain et al. teaches:
Fig. 11, ref. “Category”…
PNG
media_image1.png
248
420
media_image1.png
Greyscale
Where Fig. 11 is for the recipient (address)…
“FIG. 11, shows a diagram of an interface 1100 for intelligent and automatic electronic communication support, according to an example embodiment. Interface 1100 may be an example digest summary. For example, a recipient, as described herein, may receive one or more support requests for which the recipient is determined as the owning/responsible party. These support requests may be displayed to the recipient in interface 1100. Interface 1100 includes a dashboard 1102 and a listing section 1104. Dashboard 1102 may include selectable options, e.g., buttons, allowing or enabling the recipient to perform different operations, such as and without limitation, creating a support request, searching for a support request(s), replying to or forwarding a support request(s), providing feedback for automatically and intelligently generated responses to a support request(s), viewing metrics grading automatically and intelligently generated responses to a support request(s), and/or marking a support request(s) as resolved.” [0155]
Regarding claim 4
The method of claim 1 wherein performing the textual message disposition strategy comprises causing the defined textual message to be delivered to a person who Is not a service principal.
Jain et al. teaches:
Example of mis-routings to incorrect recipients….
“…When the correct owner (e.g., a recipient) for a request does not receive notice of the request quickly, the TTE increases—that is, the time-to-engage the request after its submission and begin resolution by the correct support group is negatively impacted by mis-routings of request to incorrect recipients such as owners/support groups. Likewise, if a support group that is not the correct owner receives a request and begins work for resolution thereof, this group may not provide a correct solution/resolution for the request or may spend time on the request before realizing the request should be re-routed to a different, correct owner, again impacting the TTE. This in turn also increases the TTR for requests, i.e., resolving requests may be directly impacted by mis-routings. In embodiments, the TTR may be considered as the time from the submission of a request to the resolution of the request…” [0031]
Regarding claim 5
The method of claim 1 wherein performing the textual message disposition strategy comprises:
discarding the defined message without delivering it to any person.
Not correct solution and then re-route request (therefore, discard the solution)…
“…When the correct owner (e.g., a recipient) for a request does not receive notice of the request quickly, the TTE increases—that is, the time-to-engage the request after its submission and begin resolution by the correct support group is negatively impacted by mis-routings of request to incorrect recipients such as owners/support groups. Likewise, if a support group that is not the correct owner receives a request and begins work for resolution thereof, this group may not provide a correct solution/resolution for the request or may spend time on the request before realizing the request should be re-routed to a different, correct owner, again impacting the TTE. This in turn also increases the TTR for requests, i.e., resolving requests may be directly impacted by mis-routings. In embodiments, the TTR may be considered as the time from the submission of a request to the resolution of the request…” [0031] Inherent with re-routed to a different, correct owner, and spend time on the request is not sending the incorrect or incomplete solution, therefore, discarding the message.
Regarding claim 10
One or more instances of non-transitory computer-readable media collectively having contents configured to cause a computing system integrated into a healthcare system to perform a method, the one or more instances of non-transitory computer-readable media, the method comprising:
receiving a textual message and an addressee service principal of the textual message via a first user interface element associated with a computing device;
{
From Applicant’s specification on “element”…
“While Figure 5 and each of the display diagrams discussed below show a display whose formatting, organization, informational density, etc., is best suited to certain types of display devices, those skilled in the art will appreciate that actual displays presented by the facility may differ from those shown, in that they may be optimized for particular other display devices, or have shown visual elements omitted, visual elements not shown included, visual elements reorganized, reformatted, revisualized, or shown at different levels of magnification, etc.” (pg. 10, lines 10-16)
Therefore, an element from Fig. 5 appears to be something (text, icon, etc.) that is displayed.
}
Jain et al. teaches:
Providing (receiving) support request (textual message) addressed to single support account or entire support team (addressee of service principal)…
“…As referred to herein, a “sender” may be any type of user or automated mechanism for providing support requests and/or information related thereto. Often times, the systems and services may receive large numbers, e.g., hundreds, thousands, or tens of thousands, of support requests from senders. When senders are not able to determine a specific owner/recipient for their support request, e.g., emails may be addressed to a single support email account for all services/products rather than a specific team or may be addressed to an entire support team instead of a specific feature owner(s) within the team, mis-routing or slow routing of support requests can occur which increases TTE (e.g., Time-to-Engage) and TTR (e.g., Time-to-Resolve) and can negatively impact the user. When the correct owner (e.g., a recipient) for a request does not receive notice of the request quickly, the TTE increases—that is, the time-to-engage the request after its submission and begin resolution by the correct support group is negatively impacted by mis-routings of request to incorrect recipients such as owners/support groups…” [0031]
Computing devices with GUIs (graphical user interfaces) to receive requests…
“Remote device 102a and remote device 102b may be any type of computing device or computing system, including a terminal, a personal computer, a laptop computer, a tablet device, a smart phone, etc., that may be used to provide support requests, e.g., via communication client 112a and/or communication client 112b, in which a sender includes support request information. For instance, as shown in FIG. 1, remote device 102a includes communication client 112a, and remote device 102b includes communication client 112b. In embodiments, remote device 102a and remote device 102b are configured to respectively activate communication client 112a and/or communication client 112b to enable a user to provide information in a support request that is used to perform intelligent and automatic handling thereof. In some embodiments, remote device 102a and remote device 102b are configured to respectively receive interfaces such as GUIs from host server 104 to enable a user to provide information in a support request that is used to perform intelligent and automatic handling thereof. That is, communication client 112a and/or communication client 112b may operate independently of host server 104. In embodiments, remote device 102a and/or remote device 102b may include a stored instance of a communication client, as described above, which may be received from host server 104. In embodiments, communication client 112a and/or communication client 112b may be any type of electronic communication client or electronic communication application, such as email clients, messaging applications, portals, and/or the like.” [0040]
See First, Second, and Third Interfaces below.
See Integrated Healthcare below.
displaying, via a second user interface element associated with the computing device, at least information relating to a timeframe and a cost notification by the addressee service principal for responding to the textual message;
{
From Applicant’s specification…
“Figure 6 is a display diagram showing sample contents of a display presented by the facility in some embodiments in response to the user activating messaging control 513 for a particular service principal. The display 600 presents 20 information 601 establishing expectations for the message that is to be sent, including the amount of time it can take to receive a response, and the fact that sending the message may result in being charged. The display includes a next control 602 that the user can activate to proceed to prepare the message.” (pg. 10, lines 17-23)
Therefore, a message with time it takes to respond and amount may be charged.
}
[No Patentable Weight is given to non-functional descriptive claim language of “at least information relating to a timeframe and a cost notification by the addressee service principal for responding to the textual message;” as this is just displaying information without functional use.]
Requests such as for billing (cost information) and support…
“The techniques and embodiments described herein provide for intelligently and automatically supporting electronic communication requests (also “requests” or “support requests” herein), such as but not limited to, electronically mailed (“emailed”) support requests, technical support requests, postings on messaging threads or forums such as those hosted by websites, social media postings, instant messages, conversations with automated mechanisms such as “bots,” billing, feedback, notifications, etc., that include requests such as for support, information, user access, and/or the like…” [0031]
Second electronic communication (second user interface) with response…
“In step 310, a second electronic communication is generated that includes the second information and the second electronic communication is provided to at least one of the sender or the recipient, and/or the first electronic communication is provided to the recipient. For example, responder 416 of FIG. 4 may be configured to generate an electronic communication for reply to the sender of the support request that includes technical support information as determined in step 308 by featurizer/selector 408. In embodiments, the electronic communication for reply to the sender may also be provided to the determined recipient for support assistance/resolution. Responder 416 may also be configured to provide the support request to the determined recipient, e.g., in cases of first impression for technical/support issues in which similar issues have never before been provided in electronic communications. Second electronic communications may also be personalized for the sender, as described herein.” [0087]
See Timeframe and Cost below.
subjecting the textual message to a machine learning classification model to determine a message category of the textual message, wherein the machine learning classification model is trained to classify an input message into a plurality of message categories
comprising at least two categories selected form the group consisting of: a prescription, a test result question, a non-urgent medical question, and a personal note, each of the plurality of message categories having a textual message disposition strategy;
Where request information (message) lacks information to correctly identify owner/team (therefore, before sending textual message to appropriate party), machine learning consumes vector information…
“The embodiments described herein provide for several techniques for properly and automatically routing support requests, and responding to support requests with technical support information, in an intelligent manner. Such techniques allow for scaling to large numbers of services, handling unstructured user inputs, and making accurate routing decisions based on limited information. For instance, to scale to large numbers of services, a communication support system may be configured to provide tracking workflows for hundreds to thousands of services where each service in turn may have several associated support teams or groups. In embodiments, for users or automated mechanisms creating and providing support requests via communication clients, the communication support system is configured to overcome difficulties in providing support requests to correct owners/recipients for support of features/products/systems/services. Because there may not be enough support request information available to manually identify the correct issue owner/team by the sender (e.g., a user notes specific system or service performance feature problems, but the underlying root cause could have been a problem in network, storage, other broad sets of services, etc.), the described techniques and embodiments provide an architecture configured to automatically accomplish such a task based on machine-learning algorithms that consume feature vectors for provided information.” [0034]
Where feature vectors are classified using classification models…
“Models/algorithms, such as classification models/algorithms, may be trained offline for deployment and utilization as described herein, according to one or more featurization operations described herein for structuring input data and determining feature vectors, and model trainer 804 may be configured to train models/algorithms using described machine learning techniques, according to embodiments. The techniques and embodiments herein may also operate according to one or more machine learning models/algorithms, such as, but without limitation, ones of the MicrosoftML machine learning models/algorithms package, Microsoft® Azure® machine learning models/algorithms, etc., from Microsoft Corporation of Redmond, Wash.: [0130]
Where remote device to provide support requests (textual message) may be portals…
“Remote device 102a and remote device 102b may be any type of computing device or computing system, including a terminal, a personal computer, a laptop computer, a tablet device, a smart phone, etc., that may be used to provide support requests, e.g., via communication client 112a and/or communication client 112b, in which a sender includes support request information. For instance, as shown in FIG. 1, remote device 102a includes communication client 112a, and remote device 102b includes communication client 112b. In embodiments, remote device 102a and remote device 102b are configured to respectively activate communication client 112a and/or communication client 112b to enable a user to provide information in a support request that is used to perform intelligent and automatic handling thereof. In some embodiments, remote device 102a and remote device 102b are configured to respectively receive interfaces such as GUIs from host server 104 to enable a user to provide information in a support request that is used to perform intelligent and automatic handling thereof. That is, communication client 112a and/or communication client 112b may operate independently of host server 104. In embodiments, remote device 102a and/or remote device 102b may include a stored instance of a communication client, as described above, which may be received from host server 104. In embodiments, communication client 112a and/or communication client 112b may be any type of electronic communication client or electronic communication application, such as email clients, messaging applications, portals, and/or the like.” [0040]
Example of classification trained algorithms for determining (predicting) routing requests…
“Classification models/algorithms may be trained, offline in some embodiments, for deployment, according to one or more featurization operations used by communication supporter 208 for structuring input data, and model trainer 220 may be configured to train models using machine learning techniques and instance weighting, according to embodiments. In embodiments, classification models may be or may comprise algorithms, such as machine-learning algorithms, for automatically and intelligently determining recipients for routing electronic communication support requests. Further details concerning model training are provided below.” [0049]
Relevant support information provided to user, and provide prior communication information to the sender (therefore, before user activates a user interface element) and provide routing support requests to correct feature owner recipients based on algorithm/model outputs…
“Methods for automatic and intelligent electronic communication support, including using machine learning, are performed by systems and apparatuses. The methods intelligently and automatically route electronic communication support requests and intelligently and automatically provide senders with information related to their support requests. The methods generate feature vectors from cleaned request information via featurization techniques, and utilize machine-learning algorithms/models and algorithm/model outputs based on the input feature vectors. Based on the algorithm/model outputs and personalized to the specific sender, relevant support information is automatically provided to the sender. The methods also determine a set of prior communications related to the support request based on a similarity measure, and provide prior communication information to the sender. The methods also include routing support requests to correct feature owner recipients based on the algorithm/model outputs.” [0003] Inherent with routing to correct owner is not sending the communication information before it subjects the document to machine learning model to predict category to which document belongs.
Monitor electronic messages including listen for and received by server new support requests, therefore, sending message not necessary…
“Notifier 404 is configured to monitor electronic messages received at server 402, such as support request communications (e.g., emails). In embodiments, notifier 404 may include or utilize functionality of an API for an exchange web service, e.g., StreamingNotification offered by Microsoft Corporation of Redmond, Wash., to listen for and receive new support requests from server 402. When a new support request is received by server 402, notifier 404 is configured to store the received request in DB 406 for later use/reference, and to alert and provide the received support request to featurizer/selector 408. In embodiments, notifier 404 may be included as a component of system 200 of FIG. 2, e.g., as part of communication supporter 208.” [0074]
Responder provide responses to senders based on recipient predictions…
“Responder 416 may be configured to perform the functions and operations of responder 230 of FIG. 2. For example, responder 416 may be configured to automatically generate electronic messages that respond to received support requests and/or to provide received support requests to recipients. In embodiments, responder 230 may provide received support requests via transmitter 418 to recipients in support groups based on recipient predictions of featurizer/selector 408 (e.g., a machine-learning classifier). Responder 416 may be configured to generate and provide responses to support requests (as responsive electronic communications) to senders and/or recipients in support groups based on recipient predictions of selector 212. These generated responses may include automatically selected technical support information obtained by featurizer/selector 408 from DB 406 and/or previously-received support requests (or communication threads associated with the previously-received support requests) obtained by featurizer/selector 408 via locator 412.” [0079]
Training information into categories…
“Referring again to flow diagram 600 of FIG. 6, training portion 602 of flow diagram 600 begins with the receipt of training data/testing data 606 (“data 606”). Data 606 may comprise previously-received support requests and/or resolutions, senders and recipients thereof, as well as communication threads thereof, a priori information, tailored support requests, etc., divided into known categories/taxonomies corresponding to self-help content for previously-identified problems/issues (e.g., “training data”). Data 606, or a portion thereof, may also be tagged with class labels as “testing data” or “training data” for modeling purposes. In embodiments, when prior support requests and response communications for resolution are identified for new support requests received, as described herein, the start and end indices for answer strings in the prior responses may be annotated as training data. Data 606 may be provided to a cleaner 608.” [0121]
Reduced network routing based on intelligently and automatically determining recipients, therefore, before the message is sent…
“The embodiments and techniques described herein provide improved performance of computing devices and operations executing thereon. By one or more of the techniques and embodiments described, recipients are predicted and technical support information is obtained intelligent and automatic electronic communication support, including using machine-learning, e.g., for support requests, in ways that reduce usage for system resources and also improve system operations. For instance, as noted above, the number of possible recipients for support requests may vary greatly from a relatively small number to thousands of support groups, staff members, and/or engineers. The recipients, according the techniques and embodiments herein, are intelligently and automatically predicted based on an incoming support request and stored support request communication threads. By intelligently and automatically determining recipients for the routing of and responding to support requests, load due to mis-routings is significantly reduced for the network utilized by technical support groups and the associated recipients. Additionally, TTE and TTR are reduced thereby improving productivity and operations of features/products/systems/services for which support requests are provided by senders. That is, issues for features/products/systems/services accessed by senders may be timely mitigated thus increasing both features/products/system/service operational efficiency as well as operational quality.” [0158]
See Questions below.
performing the textual message disposition strategy corresponding to the message category determined for the textual message;
{
Applicant’s specification does not teach “message disposition strategy.
From Applicant’s original Claim 1 (filed 09/22/2022)…
“performing the textual message disposition strategy to which the accessed mapping maps the determined textual message category with respect to the defined textual message.”
Therefore, the disposition strategy maps the message category tot the text.
}
Support requests (text messages) mapped to services (categories)…
“In step 306, the feature vector is provided as an input to a machine-learning model that automatically determines a model output based on the feature vector. For example, ML model 410 may generate this model output through a classification of the support request, based on the feature vector, into one or more predefined taxonomies determined during the training of model 410. That is, support requests may be mapped to features/products/systems/services by way of ML model 410. In embodiments, featurizer/selector 408 of FIG. 4 may be configured to automatically determine a recipient(s) based on the feature vector generated in step 304. Featurizer/selector 408 may be configured to process the feature vector according to an algorithm or model to generate an output for predicting the correct recipient(s) for the support request. According to embodiments, featurizer/selector 408 may utilize ML model 410 in making the prediction. ML model 410 may be a classifier, in embodiments, such as a machine-learning classifier that utilizes machine learning techniques based on a learning model or classification model. ML model 410 is configured to provide its output (e.g., a correct recipient prediction) to featurizer/selector 408.” [0085]
Support requests provided to categories of support requests…
“In embodiments, feature/product/service/system owners and/or teams, e.g., recipients of support requests, may be notified about tasks/work items in their respective areas of support provision. For example, tasks/work items may include support requests as described herein. The techniques and embodiments described provide for a digest summary (e.g., updated hourly, daily, or otherwise) of support request that may be provided to the owning teams/engineers for each of the categories of support requests.” [0154]
in response to detecting that the machine learning classification model fails to determine the message category of the textual message with a confidence level exceeding a selected threshold value, displaying, via a third user interface element associated with the computing device, at least a portion of the plurality of message categories and receiving additional user input indicating a message category selected; and
{
From Applicant’s specification on threshold and additional input…
“FIG. 4 is a flow diagram showing a process performed by the facility in some embodiments to process a draft message prepared by a user that is initially addressed to a service principal. In act 401, the facility receives the draft message and its original addressee. For example, in some embodiments, the user types this information into a web page or application form provided by the facility, and/or an electronic medical record (“EMR”) program. In act 402, the facility applies the trained machine learning model to the draft message received in act 401 to predict the category of the draft message. In some embodiments, the model returns a confidence level for each of the message categories indicating the likelihood that the message is a member of that category. In some embodiments, the facility selects as the category of a message any categories having a confidence level higher than a particular threshold, such as 90%. In some embodiments, the facility selects as the category of the message the category having the highest confidence level. In act 403, the facility applies to the draft message the disposition specified for the category predicted in act 402, such as the disposition identified by the category disposition table shown in FIG. 3. After act 403, this process concludes.” [0032]
“FIG. 11 is a display diagram showing sample contents of a display presented by the facility in some embodiments to permit a user to select the appropriate category for a message if predicting the message category automatically is unsuccessful, such as where it produces no category predictions with an adequate confidence level. The display 1100 includes a third message 1110; a prompt 1120 to select message category; and available categories 1130 from among which the user can choose.” [0041]
The above teaches select appropriate category, if predicting category is unsuccessful, or no category with an adequate confidence level.
}
Model updated based on additional (second) requests (input)…
“According to embodiments, model 222 may comprise one or more models or templates, as described herein, and may be stored by memory 206. Model 222 may be incrementally, or wholly, updated by model trainer 220 based on feedback, additional electronic communication support requests received, and/or the like.” [0060]
Mis or incomplete routings for support requests…
“Reporter 216 may be configured to provide re-route indications of mis- and/or incomplete-routings for support requests to recipients and/or to model training components, such as model trainer 220, and/or as described in detail below (e.g., an evaluator as described in FIG. 6). Reporter 216 may also be configured to determine and provide metrics related to a support request to model trainer 220. Reporter-determined metrics for a support request may include TTE, TTR, a number of mis-routings, portions of sender and/or recipient feedback, support request information, and/or the like.” [0066]
Solicit feedback from senders with text and options (second text message)…
“Responder 230 may also be configured to solicit feedback from senders through generated responses to support requests. Solicitations may be made by text and/or selectable options that would indicate the feedback of the sender with respect to resolution and technical support information provided for a support request.” [0069]
See Fail below.
processing the textual message corresponding to the textual message disposition strategy associated with the message category selected,
wherein order of the processing the textual message comprises one or more of acts selected from the group consisting of: rearranged, performed in parallel, omitted, included, divided, and combined.
{
Applicant’s specification does not teach “message disposition strategy.
From Applicant’s original Claim 1 (filed 09/22/2022)…
“performing the textual message disposition strategy to which the accessed mapping maps the determined textual message category with respect to the defined textual message.”
Therefore, the disposition strategy maps the message category tot the text.
}
Support requests (text messages) mapped to services (categories)…
“In step 306, the feature vector is provided as an input to a machine-learning model that automatically determines a model output based on the feature vector. For example, ML model 410 may generate this model output through a classification of the support request, based on the feature vector, into one or more predefined taxonomies determined during the training of model 410. That is, support requests may be mapped to features/products/systems/services by way of ML model 410. In embodiments, featurizer/selector 408 of FIG. 4 may be configured to automatically determine a recipient(s) based on the feature vector generated in step 304. Featurizer/selector 408 may be configured to process the feature vector according to an algorithm or model to generate an output for predicting the correct recipient(s) for the support request. According to embodiments, featurizer/selector 408 may utilize ML model 410 in making the prediction. ML model 410 may be a classifier, in embodiments, such as a machine-learning classifier that utilizes machine learning techniques based on a learning model or classification model. ML model 410 is configured to provide its output (e.g., a correct recipient prediction) to featurizer/selector 408.” [0085]
Support requests provided to categories of support requests…
“In embodiments, feature/product/service/system owners and/or teams, e.g., recipients of support requests, may be notified about tasks/work items in their respective areas of support provision. For example, tasks/work items may include support requests as described herein. The techniques and embodiments described provide for a digest summary (e.g., updated hourly, daily, or otherwise) of support request that may be provided to the owning teams/engineers for each of the categories of support requests.” [0154]
Parallel orders of operation processing…
“As illustrated in FIG. 4, an exemplary, numbered order of operations is provided, according to an embodiment. However, it should be noted that alternate orders of operation are also contemplated herein, e.g., parallel and/or serial orders, or any combination thereof) and the illustrated embodiment is not to be considered limiting.” [0081]
Integrated Healthcare
Jain et al. teaches service provider. They do not teach computer device integrated with healthcare system.
Ewin et al. also in the business of service provider teaches:
“The medical evaluation form can be communicated to a doctor via a network, such as the Internet. For example, the medical evaluation form can be communicated to a doctor via email. Alternatively, the medical evaluation form can be communicated to a doctor via facsimile.” [0024]
Server with client (integrated healthcare system)…
“Referring now to FIG. 4, a server 41 is in communication with the Internet 42 and a client 43 is also in communication with the Internet 42. Server 41 and client 43 are configured so as to facilitate filling out a medical evaluation form online and facilitate reviewing the medical information form online. Server 41 can be in either wired (as shown) or wireless communication with the Internet 42. Similarly, client 43 can be in either wired or wireless (as shown) communication with the Internet 42.” [0091]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of Jain et al. the ability to use an integrated system as taught by Ewin et al. 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. Further motivation is provided by Ewinl et al. who teaches the benefits such a system for patients asking healthcare questions.
Timeframe and Cost
Jain et al. teaches service provider. They do not teach timeframe and cost.
Shour also in the business of service provider teaches:
“In embodiments methods and systems are provided for facilitating knowledge exchange. The methods and systems may include establishing a process for allowing a service requester to request a knowledge-based service from a service provider; establishing a prerequisite data set desired by the service provider for assisting the service provider in determining whether to accept a request for service; and routing a service request to a service provider based on completed data sets of a service requester. The service may be provided online, or through a combination of online and offline steps (e.g., store and forward or store and retrieve models). References to online services throughout should be understood to encompass completely online services, as well as services that combine online with offline steps.” [0023]
Response time to service requests and fees (costs)…
“When all data requirements are entered, the professional 1002 proceeds to the step 1028, where the professional 1002 defines the response time that the professional 1002 expects to achieve in meeting service requests. For example, an engineer professional 1002 might indicate that she will respond to queries within 48 hours, or for non-emergency situations within two or more weeks. The administrator for a given enterprise or sub-enterprise can later use these response times to help monitor performance and send alerts as response times are approached without appropriate action. Next, at a step 1030, the professional 1002 defines fees for the services that the professional 1002 will provide, such as hourly fees, transaction-based fees, contingency fees, time and materials fees, and other fees.” [0078]
Example of interfaces for display…
“Referring to FIG. 7, a schematic diagram shows high-level system components for a computer system 700 to support an intelligent knowledge exchange market. The computer system 700 includes system elements for various elements that participate in the marketplace. Thus, there is a host system 702 that facilitates performance of the various functions of the host 2002 of the marketplace. There is also a plurality of enterprise systems 704 that facilitate functions of the enterprises 2004 that participate in the marketplace. Similarly, there are a plurality of office systems 708 to facilitate functions of offices 2008 and a plurality of professional systems 710 that facilitate functions of professionals 2010. These systems may be connected by a communications facility, such as a network 712, which in an embodiment is the Internet, but which in other embodiments might be a portion of the Internet, such as the worldwide web, or a wide area network, local area network, wireless network, intranet, dedicated line, or other network or communication facility for allowing connection between the various entities. In embodiments, the system for the marketplace is a web-based system, with each of the host system 702, enterprise system 704, professional system 710, and office system 708 comprising not separate systems, but rather interfaces for interaction with the central web-based system. A suitable interface might be as simple as a browser or similar facility for providing web access. In other embodiments the interfaces may be provided using a client computer program downloaded onto the various computer systems, with suitable interface software for providing an interface to the overall system. In embodiments the software may be served to the various entities by an ASP model or similar facility.” [0064]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of Jain et al. the ability to provide a timeframe and cost as taught by Shour 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. Further motivation is provided by Shour who teaches the financial benefit of determining both time and cost for providing a service to a client.
Questions
The combined references teach questions. They do not teach specific questions.
Ewin et al. also in the business of questions teaches:
Patient with questions for doctor (personal note) or drug interactions (prescription question), need for a follow-up visit (non-urgent medical questions)…
“Medical questions can be answered online. The medical questions can comprise questions either from the patient or from medical personnel. For example, a patient may have questions for the doctor about the seriousness of a condition, treatment options, drug interactions, contraindications, or side-affects, and/or the need for a follow-up visit. A doctor may have questions for the patient regarding the effectiveness of treatment, the presence of drug side-effects, and/or the progression of an illness.” [0036]
Request (question) prescription…
“Prescriptions can be requested and/or filled online. Prescriptions refills can be requested online. For example, a request for a refill can be sent to a doctor's office for authorization, if necessary, and then forwarded to a pharmacy where it is filled.” [0037]
Email for interaction…
“According to one or more embodiments, the present invention facilitates the ability for an online health care provider and patient interaction that becomes a medical evaluation, with the judgment of the practitioner having a much more sound basis since a more thorough history is available according to the present invention than what occurs according to contemporary practice via a phone, email or other non-face-to-face evaluation.” [0092]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability for patients to ask healthcare questions as taught by Ewin et al. 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. Further motivation is provided by Ewin et al. who teaches the benefits to patients of asking healthcare questions.
Fail
The combined references teach machine learning. They do not teach fail.
Fleming et al. also in the business of classification teaches;
Threshold confidence value not satisfied (fail) for classification model and display request additional user input…
“Once documents are labeled, such as via user input as described above, one or more processes may be performed to predict the classification of other documents in a document set. For example, the system may receive user input data indicating in class documents and out of class documents from a subset of first documents. For example, if the first documents include 1,000 documents, the user input data may indicating classification for a subset, such as 20, of those documents. The system may then utilize that user input data to train a classification model, such that the classification model is configured to determine whether a given document is more similar to those documents marked in class or more similar to those documents marked out of class. Utilizing the classification model, as trained, the system may predict the classification of the remainder of the first documents that were not labeled by the user input. Each or some of the predictions for the remainder of the documents may be associated with a confidence value indicating how confident the system is that the classification model accurately determined the classification of a given document. A threshold confidence value may be determined and the system may determine whether an overall confidence value associated with the classification model satisfies that threshold confidence value. In instances where the confidence value does not satisfy the threshold confidence value, the system may cause an indication of this determination to be displayed and may request additional user input data for retraining the classification model. In instances where the confidence value satisfies the threshold confidence value, the system may receive second documents for classification prediction. The second documents may be received based at least in part on a user uploading additional documents and/or from the system retrieving additional documents from one or more databases. The classification model may then be utilized to predict classification of this second document set.” (col. 8, lines 13-47)
A user interface with categories that a user may select for training purposes…
“In addition to the techniques for training the classification models described above, the classification models may also be trained and/or organized based at least in part on classifications of the documents. For example, when the documents are patents and patent applications, a predetermined classification system may be established for classifying the subject matter of a given document. The classification system may be determined by the platform, by one or more users, and/or by a third party. For example, patents and patent application may be associated with a predefined classification system such as the Cooperative Patent Classification (CPC) system. The CPC system employs CPC codes that correspond to differing subject matter, as described in more detail herein. The CPC codes for a given document may be identified and the categories associated with those codes may be determined. A user interface may be presented to the user that presents the determined categories and allows a user to select which categories the user finds in class for a given purpose. The selected categories may be utilized as a feature for training the classification models. Additionally, or alternatively, the platform may determine the CPC codes for documents marked as in class and may train the classification models to compare those CPC codes with the CPC codes associated with the documents to be analyzed to determine classification.” (col. 9, lines 41-65)
Where user selects documents class and confidence values based on the class…
“… Once a model is selected, the user may start providing indications of which documents 102 are in class and which documents 102 are out of class. These indications may be utilized to train the selected model. However, depending on the amount and quality of the user indications, the output of the trained model may not be associated with a high confidence value. In these examples, the status may indicate that the model has been trained but is not yet stable. Once the confidence values increase as the model is retrained, the status may indicate that the model is stable.” (col. 12, lines 6-15)
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to determine a threshold confidence value as taught by Fleming et al. 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. Further motivation is provided by Fleming who teaches the benefits of determining a confidence value in order to determine if further information is needed for classification purposes. It’s also useful when training machine learning models as this improves the predictiveness of output of such models.
First, Second, and Third Interfaces
The combined references teach display and interfaces. They do not explicitly teach first, second, and third interface elements. However one of ordinary skill in the art would recognize that the interaction between a user and service provider would provide various interface elements.
It would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s filing to modify the combined references with the knowledge available to such an artisan that first, second, and third interfaces could be used when for interactions between a user and a service provider. This would have been known work in the field of endeavor prompting variations of it in the same field based on use of providing information on a display and would provide predictable results.
Regarding claim 13
The one or more instances of non-transitory computer-readable media of claim 10, the method further comprising:
from among a plurality of mappings each from one of the plurality of textual message categories to a textual message disposition strategy, accessing a mapping from the determined textual message category; and
Jain et al. teaches:
Example of each service may have support teams or groups (manual) to route and support (categorize) information…
“The embodiments described herein provide for several techniques for properly and automatically routing support requests, and responding to support requests with technical support information, in an intelligent manner. Such techniques allow for scaling to large numbers of services, handling unstructured user inputs, and making accurate routing decisions based on limited information. For instance, to scale to large numbers of services, a communication support system may be configured to provide tracking workflows for hundreds to thousands of services where each service in turn may have several associated support teams or groups. In embodiments, for users or automated mechanisms creating and providing support requests via communication clients, the communication support system is configured to overcome difficulties in providing support requests to correct owners/recipients for support of features/products/systems/services. Because there may not be enough support request information available to manually identify the correct issue owner/team by the sender (e.g., a user notes specific system or service performance feature problems, but the underlying root cause could have been a problem in network, storage, other broad sets of services, etc.), the described techniques and embodiments provide an architecture configured to automatically accomplish such a task based on machine-learning algorithms that consume feature vectors for provided information.” [0034]
performing the textual message disposition strategy to which the accessed mapping maps the determined textual message category with respect to the defined textual message.
Provide feedback (performing textual message disposition) to support request…
“For example, a user of a system or a service, e.g., a cloud-based service, may have a problem with the behavior, features, operations, and/or the like, e.g., user access, of the system/service, and this problem may impact the productivity or business functions of the user. The user may provide a technical support request communication to the host and/or provider of the system or service. The host and/or provider of the system or service may desire to return the user to normal operations and productivity levels as soon as possible to avoid negative impacts to users and/or their businesses. However, the nature of the support request requires that the correct owner of the problem, feature, issue, etc., receive the support request and its information (i.e., be predicted as the recipient) for resolution, and as noted above, there may be hundreds or thousands of possible recipients for support requests. Similarly, routing for bug reporting, another type of “support request,” as well as feedback routing related to resolutions of support requests, include similar considerations for determining the correct recipient.” [0032]
Regarding claim 14
The one or more instances of non-transitory computer-readable media of claim 13 wherein performing the textual message disposition strategy comprises causing the defined textual message to be delivered to the addressee service principal with information identifying the determined textual message category.
Jain et al. teaches:
Fig. 11, ref. “Category”…
PNG
media_image1.png
248
420
media_image1.png
Greyscale
Where Fig. 11 is for the recipient (address)…
“FIG. 11, shows a diagram of an interface 1100 for intelligent and automatic electronic communication support, according to an example embodiment. Interface 1100 may be an example digest summary. For example, a recipient, as described herein, may receive one or more support requests for which the recipient is determined as the owning/responsible party. These support requests may be displayed to the recipient in interface 1100. Interface 1100 includes a dashboard 1102 and a listing section 1104. Dashboard 1102 may include selectable options, e.g., buttons, allowing or enabling the recipient to perform different operations, such as and without limitation, creating a support request, searching for a support request(s), replying to or forwarding a support request(s), providing feedback for automatically and intelligently generated responses to a support request(s), viewing metrics grading automatically and intelligently generated responses to a support request(s), and/or marking a support request(s) as resolved.” [0155]
Regarding claim 15
The one or more instances of non-transitory computer-readable media of claim 13 wherein performing the textual message disposition strategy comprises causing the defined textual message to be delivered to a person who is not a service principal.
Jain et al. teaches:
Example of mis-routings to incorrect recipients….
“…When the correct owner (e.g., a recipient) for a request does not receive notice of the request quickly, the TTE increases—that is, the time-to-engage the request after its submission and begin resolution by the correct support group is negatively impacted by mis-routings of request to incorrect recipients such as owners/support groups. Likewise, if a support group that is not the correct owner receives a request and begins work for resolution thereof, this group may not provide a correct solution/resolution for the request or may spend time on the request before realizing the request should be re-routed to a different, correct owner, again impacting the TTE. This in turn also increases the TTR for requests, i.e., resolving requests may be directly impacted by mis-routings. In embodiments, the TTR may be considered as the time from the submission of a request to the resolution of the request…” [0031]
Regarding claim 16
The one or more instances of non-transitory computer-readable media of claim 13 wherein performing the textual message disposition strategy comprises:
discarding the defined message without delivering it to any person.
Not correct solution and then re-route request (therefore, discard the solution)…
“…When the correct owner (e.g., a recipient) for a request does not receive notice of the request quickly, the TTE increases—that is, the time-to-engage the request after its submission and begin resolution by the correct support group is negatively impacted by mis-routings of request to incorrect recipients such as owners/support groups. Likewise, if a support group that is not the correct owner receives a request and begins work for resolution thereof, this group may not provide a correct solution/resolution for the request or may spend time on the request before realizing the request should be re-routed to a different, correct owner, again impacting the TTE. This in turn also increases the TTR for requests, i.e., resolving requests may be directly impacted by mis-routings. In embodiments, the TTR may be considered as the time from the submission of a request to the resolution of the request…” [0031] Inherent with re-routed to a different, correct owner, and spend time on the request is not sending the incorrect or incomplete solution, therefore, discarding the message.
Regarding claim 19
A system deployed within a communication network integrated into a healthcare system, the system comprising:
a computing device, comprising:
Jain et al. teaches:
Computing system…
“Accordingly, systems, apparatuses, and devices may be configured and enabled in various ways for intelligent and automatic handling of support requests. For example, FIG. 1 is a block diagram of a system 100, according to embodiments. System 100 is a computing system for intelligent and automatic handling of support requests, according to an embodiment. As shown in FIG. 1, system 100 includes a remote device 102a, a remote device 102b, a support device 114, and a host server 104, which may communicate with each other over a network 110. It should be noted that the number of remote devices and host servers of FIG. 1 is exemplary in nature, and greater numbers of each may be present in various embodiments. Additionally, any combination of components illustrated may comprise a system for intelligent and automatic handling of support requests, according to embodiments.” [0037]
a non-transitory computer-readable storage medium storing instructions; and
Memory and execut instructions…
“Processor 204 and memory 206 may respectively be any type of processor circuit or memory that is described herein, and/or as would be understood by a person of skill in the relevant art(s) having the benefit of this disclosure. Processor 204 and memory 206 may each respectively comprise one or more processors or memories, different types of processors or memories, remote processors or memories, and/or distributed processors or memories. Processor 204 is configured to execute computer program instructions such as but not limited to embodiments of communication supporter 208, e.g., as computer program instructions for automatic and intelligent electronic communication support, etc., as described herein, and memory 206 is configured to store such computer program instructions, as well as to store other information and data described in this disclosure, including but without limitation, model 222, past support requests and responses, technical support wiki-pages, frequently asked questions, information for personalization, etc.” [0046]
See Integrated Healthcare below.
a processor coupled to the non-transitory computer-readable storage medium and configured to execute the instructions to:
receive a textual message and an addressee service principal of the textual message via a first user interface element associated with the computing device;
{
From Applicant’s specification on “element”…
“While Figure 5 and each of the display diagrams discussed below show a display whose formatting, organization, informational density, etc., is best suited to certain types of display devices, those skilled in the art will appreciate that actual displays presented by the facility may differ from those shown, in that they may be optimized for particular other display devices, or have shown visual elements omitted, visual elements not shown included, visual elements reorganized, reformatted, revisualized, or shown at different levels of magnification, etc.” (pg. 10, lines 10-16)
Therefore, an element from Fig. 5 appears to be something (text, icon, etc.) that is displayed.
}
Jain et al. continues to teach:
Providing (receiving) support request (textual message) addressed to single support account or entire support team (addressee of service principal)…
“…As referred to herein, a “sender” may be any type of user or automated mechanism for providing support requests and/or information related thereto. Often times, the systems and services may receive large numbers, e.g., hundreds, thousands, or tens of thousands, of support requests from senders. When senders are not able to determine a specific owner/recipient for their support request, e.g., emails may be addressed to a single support email account for all services/products rather than a specific team or may be addressed to an entire support team instead of a specific feature owner(s) within the team, mis-routing or slow routing of support requests can occur which increases TTE (e.g., Time-to-Engage) and TTR (e.g., Time-to-Resolve) and can negatively impact the user. When the correct owner (e.g., a recipient) for a request does not receive notice of the request quickly, the TTE increases—that is, the time-to-engage the request after its submission and begin resolution by the correct support group is negatively impacted by mis-routings of request to incorrect recipients such as owners/support groups…” [0031]
Computing devices with GUIs (graphical user interfaces) to receive requests…
“Remote device 102a and remote device 102b may be any type of computing device or computing system, including a terminal, a personal computer, a laptop computer, a tablet device, a smart phone, etc., that may be used to provide support requests, e.g., via communication client 112a and/or communication client 112b, in which a sender includes support request information. For instance, as shown in FIG. 1, remote device 102a includes communication client 112a, and remote device 102b includes communication client 112b. In embodiments, remote device 102a and remote device 102b are configured to respectively activate communication client 112a and/or communication client 112b to enable a user to provide information in a support request that is used to perform intelligent and automatic handling thereof. In some embodiments, remote device 102a and remote device 102b are configured to respectively receive interfaces such as GUIs from host server 104 to enable a user to provide information in a support request that is used to perform intelligent and automatic handling thereof. That is, communication client 112a and/or communication client 112b may operate independently of host server 104. In embodiments, remote device 102a and/or remote device 102b may include a stored instance of a communication client, as described above, which may be received from host server 104. In embodiments, communication client 112a and/or communication client 112b may be any type of electronic communication client or electronic communication application, such as email clients, messaging applications, portals, and/or the like.” [0040]
See First, Second, and Third Interfaces below.
display, via a second user interface element associated with the computing device, at least information relating to a timeframe and a cost notification by the addressee service principal for responding to the textual message;
{
From Applicant’s specification…
“Figure 6 is a display diagram showing sample contents of a display presented by the facility in some embodiments in response to the user activating messaging control 513 for a particular service principal. The display 600 presents 20 information 601 establishing expectations for the message that is to be sent, including the amount of time it can take to receive a response, and the fact that sending the message may result in being charged. The display includes a next control 602 that the user can activate to proceed to prepare the message.” (pg. 10, lines 17-23)
Therefore, a message with time it takes to respond and amount may be charged.
}
[No Patentable Weight is given to non-functional descriptive claim language of “at least information relating to a timeframe and a cost notification by the addressee service principal for responding to the textual message;” as this is just displaying information without functional use.]
Requests such as for billing (cost information) and support…
“The techniques and embodiments described herein provide for intelligently and automatically supporting electronic communication requests (also “requests” or “support requests” herein), such as but not limited to, electronically mailed (“emailed”) support requests, technical support requests, postings on messaging threads or forums such as those hosted by websites, social media postings, instant messages, conversations with automated mechanisms such as “bots,” billing, feedback, notifications, etc., that include requests such as for support, information, user access, and/or the like…” [0031]
Second electronic communication (second user interface) with response…
“In step 310, a second electronic communication is generated that includes the second information and the second electronic communication is provided to at least one of the sender or the recipient, and/or the first electronic communication is provided to the recipient. For example, responder 416 of FIG. 4 may be configured to generate an electronic communication for reply to the sender of the support request that includes technical support information as determined in step 308 by featurizer/selector 408. In embodiments, the electronic communication for reply to the sender may also be provided to the determined recipient for support assistance/resolution. Responder 416 may also be configured to provide the support request to the determined recipient, e.g., in cases of first impression for technical/support issues in which similar issues have never before been provided in electronic communications. Second electronic communications may also be personalized for the sender, as described herein.” [0087]
See Timeframe and Cost below.
subject the textual message to a machine learning classification model to determine a message category of the textual message, wherein the machine learning classification model is trained to classify an input message into a plurality of message categories
comprising at least two categories selected from the group consisting of a prescription question, a test result question, a non-urgent medical question, and a personal not, each of the plurality of message categories having a textual message disposition strategy;
Where request information (message) lacks information to correctly identify owner/team (therefore, before sending textual message to appropriate party), machine learning consumes vector information…
“The embodiments described herein provide for several techniques for properly and automatically routing support requests, and responding to support requests with technical support information, in an intelligent manner. Such techniques allow for scaling to large numbers of services, handling unstructured user inputs, and making accurate routing decisions based on limited information. For instance, to scale to large numbers of services, a communication support system may be configured to provide tracking workflows for hundreds to thousands of services where each service in turn may have several associated support teams or groups. In embodiments, for users or automated mechanisms creating and providing support requests via communication clients, the communication support system is configured to overcome difficulties in providing support requests to correct owners/recipients for support of features/products/systems/services. Because there may not be enough support request information available to manually identify the correct issue owner/team by the sender (e.g., a user notes specific system or service performance feature problems, but the underlying root cause could have been a problem in network, storage, other broad sets of services, etc.), the described techniques and embodiments provide an architecture configured to automatically accomplish such a task based on machine-learning algorithms that consume feature vectors for provided information.” [0034]
Where feature vectors are classified using classification models…
“Models/algorithms, such as classification models/algorithms, may be trained offline for deployment and utilization as described herein, according to one or more featurization operations described herein for structuring input data and determining feature vectors, and model trainer 804 may be configured to train models/algorithms using described machine learning techniques, according to embodiments. The techniques and embodiments herein may also operate according to one or more machine learning models/algorithms, such as, but without limitation, ones of the MicrosoftML machine learning models/algorithms package, Microsoft® Azure® machine learning models/algorithms, etc., from Microsoft Corporation of Redmond, Wash.: [0130]
Where remote device to provide support requests (textual message) may be portals…
“Remote device 102a and remote device 102b may be any type of computing device or computing system, including a terminal, a personal computer, a laptop computer, a tablet device, a smart phone, etc., that may be used to provide support requests, e.g., via communication client 112a and/or communication client 112b, in which a sender includes support request information. For instance, as shown in FIG. 1, remote device 102a includes communication client 112a, and remote device 102b includes communication client 112b. In embodiments, remote device 102a and remote device 102b are configured to respectively activate communication client 112a and/or communication client 112b to enable a user to provide information in a support request that is used to perform intelligent and automatic handling thereof. In some embodiments, remote device 102a and remote device 102b are configured to respectively receive interfaces such as GUIs from host server 104 to enable a user to provide information in a support request that is used to perform intelligent and automatic handling thereof. That is, communication client 112a and/or communication client 112b may operate independently of host server 104. In embodiments, remote device 102a and/or remote device 102b may include a stored instance of a communication client, as described above, which may be received from host server 104. In embodiments, communication client 112a and/or communication client 112b may be any type of electronic communication client or electronic communication application, such as email clients, messaging applications, portals, and/or the like.” [0040]
Example of classification trained algorithms for determining (predicting) routing requests…
“Classification models/algorithms may be trained, offline in some embodiments, for deployment, according to one or more featurization operations used by communication supporter 208 for structuring input data, and model trainer 220 may be configured to train models using machine learning techniques and instance weighting, according to embodiments. In embodiments, classification models may be or may comprise algorithms, such as machine-learning algorithms, for automatically and intelligently determining recipients for routing electronic communication support requests. Further details concerning model training are provided below.” [0049]
Relevant support information provided to user, and provide prior communication information to the sender (therefore, before user activates a user interface element) and provide routing support requests to correct feature owner recipients based on algorithm/model outputs…
“Methods for automatic and intelligent electronic communication support, including using machine learning, are performed by systems and apparatuses. The methods intelligently and automatically route electronic communication support requests and intelligently and automatically provide senders with information related to their support requests. The methods generate feature vectors from cleaned request information via featurization techniques, and utilize machine-learning algorithms/models and algorithm/model outputs based on the input feature vectors. Based on the algorithm/model outputs and personalized to the specific sender, relevant support information is automatically provided to the sender. The methods also determine a set of prior communications related to the support request based on a similarity measure, and provide prior communication information to the sender. The methods also include routing support requests to correct feature owner recipients based on the algorithm/model outputs.” [0003] Inherent with routing to correct owner is not sending the communication information before it subjects the document to machine learning model to predict category to which document belongs.
Monitor electronic messages including listen for and received by server new support requests, therefore, sending message not necessary…
“Notifier 404 is configured to monitor electronic messages received at server 402, such as support request communications (e.g., emails). In embodiments, notifier 404 may include or utilize functionality of an API for an exchange web service, e.g., StreamingNotification offered by Microsoft Corporation of Redmond, Wash., to listen for and receive new support requests from server 402. When a new support request is received by server 402, notifier 404 is configured to store the received request in DB 406 for later use/reference, and to alert and provide the received support request to featurizer/selector 408. In embodiments, notifier 404 may be included as a component of system 200 of FIG. 2, e.g., as part of communication supporter 208.” [0074]
Responder provide responses to senders based on recipient predictions…
“Responder 416 may be configured to perform the functions and operations of responder 230 of FIG. 2. For example, responder 416 may be configured to automatically generate electronic messages that respond to received support requests and/or to provide received support requests to recipients. In embodiments, responder 230 may provide received support requests via transmitter 418 to recipients in support groups based on recipient predictions of featurizer/selector 408 (e.g., a machine-learning classifier). Responder 416 may be configured to generate and provide responses to support requests (as responsive electronic communications) to senders and/or recipients in support groups based on recipient predictions of selector 212. These generated responses may include automatically selected technical support information obtained by featurizer/selector 408 from DB 406 and/or previously-received support requests (or communication threads associated with the previously-received support requests) obtained by featurizer/selector 408 via locator 412.” [0079]
Training information into categories…
“Referring again to flow diagram 600 of FIG. 6, training portion 602 of flow diagram 600 begins with the receipt of training data/testing data 606 (“data 606”). Data 606 may comprise previously-received support requests and/or resolutions, senders and recipients thereof, as well as communication threads thereof, a priori information, tailored support requests, etc., divided into known categories/taxonomies corresponding to self-help content for previously-identified problems/issues (e.g., “training data”). Data 606, or a portion thereof, may also be tagged with class labels as “testing data” or “training data” for modeling purposes. In embodiments, when prior support requests and response communications for resolution are identified for new support requests received, as described herein, the start and end indices for answer strings in the prior responses may be annotated as training data. Data 606 may be provided to a cleaner 608.” [0121]
Reduced network routing based on intelligently and automatically determining recipients, therefore, before the message is sent…
“The embodiments and techniques described herein provide improved performance of computing devices and operations executing thereon. By one or more of the techniques and embodiments described, recipients are predicted and technical support information is obtained intelligent and automatic electronic communication support, including using machine-learning, e.g., for support requests, in ways that reduce usage for system resources and also improve system operations. For instance, as noted above, the number of possible recipients for support requests may vary greatly from a relatively small number to thousands of support groups, staff members, and/or engineers. The recipients, according the techniques and embodiments herein, are intelligently and automatically predicted based on an incoming support request and stored support request communication threads. By intelligently and automatically determining recipients for the routing of and responding to support requests, load due to mis-routings is significantly reduced for the network utilized by technical support groups and the associated recipients. Additionally, TTE and TTR are reduced thereby improving productivity and operations of features/products/systems/services for which support requests are provided by senders. That is, issues for features/products/systems/services accessed by senders may be timely mitigated thus increasing both features/products/system/service operational efficiency as well as operational quality.” [0158]
See Questions below.
perform the textual message disposition strategy corresponding to the message category determined for the textual message;
{
Applicant’s specification does not teach “message disposition strategy.
From Applicant’s original Claim 1 (filed 09/22/2022)…
“performing the textual message disposition strategy to which the accessed mapping maps the determined textual message category with respect to the defined textual message.”
Therefore, the disposition strategy maps the message category tot the text.
}
Support requests (text messages) mapped to services (categories)…
“In step 306, the feature vector is provided as an input to a machine-learning model that automatically determines a model output based on the feature vector. For example, ML model 410 may generate this model output through a classification of the support request, based on the feature vector, into one or more predefined taxonomies determined during the training of model 410. That is, support requests may be mapped to features/products/systems/services by way of ML model 410. In embodiments, featurizer/selector 408 of FIG. 4 may be configured to automatically determine a recipient(s) based on the feature vector generated in step 304. Featurizer/selector 408 may be configured to process the feature vector according to an algorithm or model to generate an output for predicting the correct recipient(s) for the support request. According to embodiments, featurizer/selector 408 may utilize ML model 410 in making the prediction. ML model 410 may be a classifier, in embodiments, such as a machine-learning classifier that utilizes machine learning techniques based on a learning model or classification model. ML model 410 is configured to provide its output (e.g., a correct recipient prediction) to featurizer/selector 408.” [0085]
Support requests provided to categories of support requests…
“In embodiments, feature/product/service/system owners and/or teams, e.g., recipients of support requests, may be notified about tasks/work items in their respective areas of support provision. For example, tasks/work items may include support requests as described herein. The techniques and embodiments described provide for a digest summary (e.g., updated hourly, daily, or otherwise) of support request that may be provided to the owning teams/engineers for each of the categories of support requests.” [0154]
in response to detecting that the machine learning classification model fails to determine the message category of the textual message with a confidence level exceeding a selected threshold value, display, via a third user interface element associated with the computing device, at least a portion of the plurality of message categories and receiving additional user input indicating a message category selected; and
{
From Applicant’s specification on threshold and additional input…
“FIG. 4 is a flow diagram showing a process performed by the facility in some embodiments to process a draft message prepared by a user that is initially addressed to a service principal. In act 401, the facility receives the draft message and its original addressee. For example, in some embodiments, the user types this information into a web page or application form provided by the facility, and/or an electronic medical record (“EMR”) program. In act 402, the facility applies the trained machine learning model to the draft message received in act 401 to predict the category of the draft message. In some embodiments, the model returns a confidence level for each of the message categories indicating the likelihood that the message is a member of that category. In some embodiments, the facility selects as the category of a message any categories having a confidence level higher than a particular threshold, such as 90%. In some embodiments, the facility selects as the category of the message the category having the highest confidence level. In act 403, the facility applies to the draft message the disposition specified for the category predicted in act 402, such as the disposition identified by the category disposition table shown in FIG. 3. After act 403, this process concludes.” [0032]
“FIG. 11 is a display diagram showing sample contents of a display presented by the facility in some embodiments to permit a user to select the appropriate category for a message if predicting the message category automatically is unsuccessful, such as where it produces no category predictions with an adequate confidence level. The display 1100 includes a third message 1110; a prompt 1120 to select message category; and available categories 1130 from among which the user can choose.” [0041]
The above teaches select appropriate category, if predicting category is unsuccessful, or no category with an adequate confidence level.
}
Model updated based on additional (second) requests (input)…
“According to embodiments, model 222 may comprise one or more models or templates, as described herein, and may be stored by memory 206. Model 222 may be incrementally, or wholly, updated by model trainer 220 based on feedback, additional electronic communication support requests received, and/or the like.” [0060]
Mis or incomplete routings for support requests…
“Reporter 216 may be configured to provide re-route indications of mis- and/or incomplete-routings for support requests to recipients and/or to model training components, such as model trainer 220, and/or as described in detail below (e.g., an evaluator as described in FIG. 6). Reporter 216 may also be configured to determine and provide metrics related to a support request to model trainer 220. Reporter-determined metrics for a support request may include TTE, TTR, a number of mis-routings, portions of sender and/or recipient feedback, support request information, and/or the like.” [0066]
Solicit feedback from senders with text and options (second text message)…
“Responder 230 may also be configured to solicit feedback from senders through generated responses to support requests. Solicitations may be made by text and/or selectable options that would indicate the feedback of the sender with respect to resolution and technical support information provided for a support request.” [0069]
See Fail below.
process the textual message corresponding to the textual message disposition strategy associated with the message category selected, wherein order of the processing the textual message comprises one or more order of acts selected from the group consisting of: rearranged, performed in parallel, omitted, included, divided and combined.
{
Applicant’s specification does not teach “message disposition strategy.
From Applicant’s original Claim 1 (filed 09/22/2022)…
“performing the textual message disposition strategy to which the accessed mapping maps the determined textual message category with respect to the defined textual message.”
Therefore, the disposition strategy maps the message category tot the text.
}
Support requests (text messages) mapped to services (categories)…
“In step 306, the feature vector is provided as an input to a machine-learning model that automatically determines a model output based on the feature vector. For example, ML model 410 may generate this model output through a classification of the support request, based on the feature vector, into one or more predefined taxonomies determined during the training of model 410. That is, support requests may be mapped to features/products/systems/services by way of ML model 410. In embodiments, featurizer/selector 408 of FIG. 4 may be configured to automatically determine a recipient(s) based on the feature vector generated in step 304. Featurizer/selector 408 may be configured to process the feature vector according to an algorithm or model to generate an output for predicting the correct recipient(s) for the support request. According to embodiments, featurizer/selector 408 may utilize ML model 410 in making the prediction. ML model 410 may be a classifier, in embodiments, such as a machine-learning classifier that utilizes machine learning techniques based on a learning model or classification model. ML model 410 is configured to provide its output (e.g., a correct recipient prediction) to featurizer/selector 408.” [0085]
Support requests provided to categories of support requests…
“In embodiments, feature/product/service/system owners and/or teams, e.g., recipients of support requests, may be notified about tasks/work items in their respective areas of support provision. For example, tasks/work items may include support requests as described herein. The techniques and embodiments described provide for a digest summary (e.g., updated hourly, daily, or otherwise) of support request that may be provided to the owning teams/engineers for each of the categories of support requests.” [0154]
Parallel orders of operation processing…
“As illustrated in FIG. 4, an exemplary, numbered order of operations is provided, according to an embodiment. However, it should be noted that alternate orders of operation are also contemplated herein, e.g., parallel and/or serial orders, or any combination thereof) and the illustrated embodiment is not to be considered limiting.” [0081]
Integrated Healthcare
Jain et al. teaches service provider. They do not teach computer device integrated with healthcare system.
Ewin et al. also in the business of service provider teaches:
“The medical evaluation form can be communicated to a doctor via a network, such as the Internet. For example, the medical evaluation form can be communicated to a doctor via email. Alternatively, the medical evaluation form can be communicated to a doctor via facsimile.” [0024]
Server with client (integrated healthcare system)…
“Referring now to FIG. 4, a server 41 is in communication with the Internet 42 and a client 43 is also in communication with the Internet 42. Server 41 and client 43 are configured so as to facilitate filling out a medical evaluation form online and facilitate reviewing the medical information form online. Server 41 can be in either wired (as shown) or wireless communication with the Internet 42. Similarly, client 43 can be in either wired or wireless (as shown) communication with the Internet 42.” [0091]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of Jain et al. the ability to use an integrated system as taught by Ewin et al. 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. Further motivation is provided by Ewinl et al. who teaches the benefits such a system for patients asking healthcare questions.
Timeframe and Cost
Jain et al. teaches service provider. They do not teach timeframe and cost.
Shour also in the business of service provider teaches:
“In embodiments methods and systems are provided for facilitating knowledge exchange. The methods and systems may include establishing a process for allowing a service requester to request a knowledge-based service from a service provider; establishing a prerequisite data set desired by the service provider for assisting the service provider in determining whether to accept a request for service; and routing a service request to a service provider based on completed data sets of a service requester. The service may be provided online, or through a combination of online and offline steps (e.g., store and forward or store and retrieve models). References to online services throughout should be understood to encompass completely online services, as well as services that combine online with offline steps.” [0023]
Response time to service requests and fees (costs)…
“When all data requirements are entered, the professional 1002 proceeds to the step 1028, where the professional 1002 defines the response time that the professional 1002 expects to achieve in meeting service requests. For example, an engineer professional 1002 might indicate that she will respond to queries within 48 hours, or for non-emergency situations within two or more weeks. The administrator for a given enterprise or sub-enterprise can later use these response times to help monitor performance and send alerts as response times are approached without appropriate action. Next, at a step 1030, the professional 1002 defines fees for the services that the professional 1002 will provide, such as hourly fees, transaction-based fees, contingency fees, time and materials fees, and other fees.” [0078]
Example of interfaces for display…
“Referring to FIG. 7, a schematic diagram shows high-level system components for a computer system 700 to support an intelligent knowledge exchange market. The computer system 700 includes system elements for various elements that participate in the marketplace. Thus, there is a host system 702 that facilitates performance of the various functions of the host 2002 of the marketplace. There is also a plurality of enterprise systems 704 that facilitate functions of the enterprises 2004 that participate in the marketplace. Similarly, there are a plurality of office systems 708 to facilitate functions of offices 2008 and a plurality of professional systems 710 that facilitate functions of professionals 2010. These systems may be connected by a communications facility, such as a network 712, which in an embodiment is the Internet, but which in other embodiments might be a portion of the Internet, such as the worldwide web, or a wide area network, local area network, wireless network, intranet, dedicated line, or other network or communication facility for allowing connection between the various entities. In embodiments, the system for the marketplace is a web-based system, with each of the host system 702, enterprise system 704, professional system 710, and office system 708 comprising not separate systems, but rather interfaces for interaction with the central web-based system. A suitable interface might be as simple as a browser or similar facility for providing web access. In other embodiments the interfaces may be provided using a client computer program downloaded onto the various computer systems, with suitable interface software for providing an interface to the overall system. In embodiments the software may be served to the various entities by an ASP model or similar facility.” [0064]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of Jain et al. the ability to provide a timeframe and cost as taught by Shour 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. Further motivation is provided by Shour who teaches the financial benefit of determining both time and cost for providing a service to a client.
Questions
The combined references teach questions. They do not teach specific questions.
Ewin et al. also in the business of questions teaches:
Patient with questions for doctor (personal note) or drug interactions (prescription question), need for a follow-up visit (non-urgent medical questions)…
“Medical questions can be answered online. The medical questions can comprise questions either from the patient or from medical personnel. For example, a patient may have questions for the doctor about the seriousness of a condition, treatment options, drug interactions, contraindications, or side-affects, and/or the need for a follow-up visit. A doctor may have questions for the patient regarding the effectiveness of treatment, the presence of drug side-effects, and/or the progression of an illness.” [0036]
Request (question) prescription…
“Prescriptions can be requested and/or filled online. Prescriptions refills can be requested online. For example, a request for a refill can be sent to a doctor's office for authorization, if necessary, and then forwarded to a pharmacy where it is filled.” [0037]
Email for interaction…
“According to one or more embodiments, the present invention facilitates the ability for an online health care provider and patient interaction that becomes a medical evaluation, with the judgment of the practitioner having a much more sound basis since a more thorough history is available according to the present invention than what occurs according to contemporary practice via a phone, email or other non-face-to-face evaluation.” [0092]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability for patients to ask healthcare questions as taught by Ewin et al. 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. Further motivation is provided by Ewin et al. who teaches the benefits to patients of asking healthcare questions.
Fail
The combined references teach machine learning. They do not teach fail.
Fleming et al. also in the business of classification teaches;
Threshold confidence value not satisfied (fail) for classification model and display request additional user input…
“Once documents are labeled, such as via user input as described above, one or more processes may be performed to predict the classification of other documents in a document set. For example, the system may receive user input data indicating in class documents and out of class documents from a subset of first documents. For example, if the first documents include 1,000 documents, the user input data may indicating classification for a subset, such as 20, of those documents. The system may then utilize that user input data to train a classification model, such that the classification model is configured to determine whether a given document is more similar to those documents marked in class or more similar to those documents marked out of class. Utilizing the classification model, as trained, the system may predict the classification of the remainder of the first documents that were not labeled by the user input. Each or some of the predictions for the remainder of the documents may be associated with a confidence value indicating how confident the system is that the classification model accurately determined the classification of a given document. A threshold confidence value may be determined and the system may determine whether an overall confidence value associated with the classification model satisfies that threshold confidence value. In instances where the confidence value does not satisfy the threshold confidence value, the system may cause an indication of this determination to be displayed and may request additional user input data for retraining the classification model. In instances where the confidence value satisfies the threshold confidence value, the system may receive second documents for classification prediction. The second documents may be received based at least in part on a user uploading additional documents and/or from the system retrieving additional documents from one or more databases. The classification model may then be utilized to predict classification of this second document set.” (col. 8, lines 13-47)
A user interface with categories that a user may select for training purposes…
“In addition to the techniques for training the classification models described above, the classification models may also be trained and/or organized based at least in part on classifications of the documents. For example, when the documents are patents and patent applications, a predetermined classification system may be established for classifying the subject matter of a given document. The classification system may be determined by the platform, by one or more users, and/or by a third party. For example, patents and patent application may be associated with a predefined classification system such as the Cooperative Patent Classification (CPC) system. The CPC system employs CPC codes that correspond to differing subject matter, as described in more detail herein. The CPC codes for a given document may be identified and the categories associated with those codes may be determined. A user interface may be presented to the user that presents the determined categories and allows a user to select which categories the user finds in class for a given purpose. The selected categories may be utilized as a feature for training the classification models. Additionally, or alternatively, the platform may determine the CPC codes for documents marked as in class and may train the classification models to compare those CPC codes with the CPC codes associated with the documents to be analyzed to determine classification.” (col. 9, lines 41-65)
Where user selects documents class and confidence values based on the class…
“… Once a model is selected, the user may start providing indications of which documents 102 are in class and which documents 102 are out of class. These indications may be utilized to train the selected model. However, depending on the amount and quality of the user indications, the output of the trained model may not be associated with a high confidence value. In these examples, the status may indicate that the model has been trained but is not yet stable. Once the confidence values increase as the model is retrained, the status may indicate that the model is stable.” (col. 12, lines 6-15)
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to determine a threshold conficence value as taught by Fleming et al. 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. Further motivation is provided by Fleming who teaches the benefits of determining a confidence value in order to determine if further information is needed for classification purposes. It’s also useful when training machine learning models as this improves the predictiveness of output of such models.
First, Second, and Third Interfaces
The combined references teach display and interfaces. They do not explicitly teach first, second, and third interface elements. However one of ordinary skill in the art would recognize that the interaction between a user and service provider would provide various interface elements.
It would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s filing to modify the combined references with the knowledge available to such an artisan that first, second, and third interfaces could be used when for interactions between a user and a service provider. This would have been known work in the field of endeavor prompting variations of it in the same field based on use of providing information on a display and would provide predictable results.
Claims 2 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over the combined references in section (10) above in further view of Pub. No. US 2022/0311728 to Sivaswamy et al.
Regarding claim 2
The method of claim 1 wherein the addressee service principal operates in a distinguished service domain, further comprising:
accessing a plurality of sample textual messages each intended for an addressee service principal in the distinguished service domain;
Jain et al. teaches:
Responses to requests include previous resolutions (plurality of sample textual messages)…
“The techniques and embodiments described herein provide for intelligently and automatically supporting electronic communication requests (also “requests” or “support requests” herein), such as but not limited to, electronically mailed (“emailed”) support requests, technical support requests, postings on messaging threads or forums such as those hosted by websites, social media postings, instant messages, conversations with automated mechanisms such as “bots,” billing, feedback, notifications, etc., that include requests such as for support, information, user access, and/or the like. That is, while embodiments herein may be described in the context of “support requests” as illustrative examples, such embodiments are also contemplated for other types of “requests,” such as but without limitation, the types noted above. In embodiments, requests may be intelligently and automatically routed to correct feature owners (i.e., recipients) of support teams, and intelligently generated automatic responses to requests may be provided to senders of support requests. Responses to requests may include information related to previous resolutions of prior support requests, as well as the prior support requests themselves…” [0031]
See Sample below.
for each of the plurality of sample textual messages:
accessing a category among the plurality of textual message categories manually assigned to the sample textual message;
Example of each service may have support teams or groups (manual) to route and support (categorize) information…
“The embodiments described herein provide for several techniques for properly and automatically routing support requests, and responding to support requests with technical support information, in an intelligent manner. Such techniques allow for scaling to large numbers of services, handling unstructured user inputs, and making accurate routing decisions based on limited information. For instance, to scale to large numbers of services, a communication support system may be configured to provide tracking workflows for hundreds to thousands of services where each service in turn may have several associated support teams or groups. In embodiments, for users or automated mechanisms creating and providing support requests via communication clients, the communication support system is configured to overcome difficulties in providing support requests to correct owners/recipients for support of features/products/systems/services. Because there may not be enough support request information available to manually identify the correct issue owner/team by the sender (e.g., a user notes specific system or service performance feature problems, but the underlying root cause could have been a problem in network, storage, other broad sets of services, etc.), the described techniques and embodiments provide an architecture configured to automatically accomplish such a task based on machine-learning algorithms that consume feature vectors for provided information.” [0034]
See Sample below.
constructing a training observation in which the sample textual message is an independent variable and the category manually assigned to the sample textual message is a dependent variable; and
Model training for classifying support requests (output, therefore, independent variable), where support request vector is an input (dependent variable)…
“Model 222 may be trained by model trainer 220, according to embodiments. Model 222 may be a classification model utilized for classifying electronic communication support requests and/or the like, for proper routing to recipients (e.g., support groups/teams/engineers) for handling. Model 222 may be configured to take a feature vector for a support request as an input from featurizer 210, and provide a model output. Model 222 may generate this model output through a classification of the support request, based on the feature vector, into one or more predefined taxonomies determined during the training of model 222. In embodiments, model 222 may also take sender-specific information as an input to personalize the response to the sender. For example, prior model outputs for communications from the sender, the communications themselves, prior recipients determined for past communications from the sender, and/or the like, may be taken into account by model 222 to personalize model outputs accordingly, resulting in personalized responses to the sender. In some embodiments, users may have specific, personalized model instances of model 222 trained according to one or more sender-specific information inputs and/or user-specific aspects of model 222 may be weighted more as inputs. Personalization may be based on one or more of the follow illustrative examples, although additional bases for personalization may be used as would become apparent of one of skill in the relevant art(s) having the benefit of this disclosure.” [0050]
See Sample below.
See Independent and Dependent below.
training the machine learning classification model using the constructed training observations.
Where the classification model is trained…
“Model 222 may be trained by model trainer 220, according to embodiments. Model 222 may be a classification model utilized for classifying electronic communication support requests and/or the like, for proper routing to recipients (e.g., support groups/teams/engineers) for handling. Model 222 may be configured to take a feature vector for a support request as an input from featurizer 210, and provide a model output. Model 222 may generate this model output through a classification of the support request, based on the feature vector, into one or more predefined taxonomies determined during the training of model 222. In embodiments, model 222 may also take sender-specific information as an input to personalize the response to the sender. For example, prior model outputs for communications from the sender, the communications themselves, prior recipients determined for past communications from the sender, and/or the like, may be taken into account by model 222 to personalize model outputs accordingly, resulting in personalized responses to the sender. In some embodiments, users may have specific, personalized model instances of model 222 trained according to one or more sender-specific information inputs and/or user-specific aspects of model 222 may be weighted more as inputs. Personalization may be based on one or more of the follow illustrative examples, although additional bases for personalization may be used as would become apparent of one of skill in the relevant art(s) having the benefit of this disclosure.” [0050]
Sample
The combined references teach training on messages. They do no literally teach sample.
Sivaswamy et al. also in the business of training on messages teaches:
“… Further, network 400 may be evaluated to quantify the performance of evaluating a dataset, such as by use of an evaluation metric (e.g., mean squared error, cross-entropy cost function, accuracy functions, confusion matrix, precision-recall curve, mean absolute error, etc.). Training of network 400, may be performed until a particular predefined accuracy threshold is met. For example, a number of epochs may need to be adjusted to ensure and validate an accuracy that is over 90%. The validation may also be performed by a user to ensure one or more of the following: removal of samples from over-represented classes (alternatively, an under-sampling technique); and adding more samples from under-represented classes (alternatively, an over-sampling technique)” [0085]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to sample messages as taught by Sivaswamy et al. 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. Further motivation is provided by Sivaswamy et al. who teaches sampling for accuracy. Jain benefits as they also teach training models based on messages for proper classification of information. Jain benefits by accurately classifying received messages.
Independent and Dependent
The combined references teach input and output and training. They do not teach independent and dependent variable.
Sivaswamy et al. also in the business of training on messages teaches:
Context of words and categories (dependent variable) dependent on messages (therefore, independent variables)…
“In some embodiments, the context of a word may be dependent on one or more previously analyzed electronic documents (e.g., messages written by users). Examples of parts of speech that may be assigned to words include, but are not limited to, nouns, verbs, adjectives, adverbs, and the like. Examples of other part of speech categories that POS tagger may assign include, but are not limited to, comparative or superlative adverbs, wh-adverbs, conjunctions, determiners, negative particles, possessive markers, prepositions, wh-pronouns, and the like. In some embodiments, the POS tagger may tag or otherwise annotate tokens of a passage with part of speech categories. In some embodiments, the POS tagger may tag tokens or words of a passage to be parsed by natural language processing.” [0065]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to consider dependent and independent values as taught by Sivaswamy et al. 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. Further motivation is provided by Jain who uses machine learning, where inherent with machine learning is training and use of messages (independent values) to determine classification/categories (dependent values).
The combined references teach machine learning and training. They do not literally teach independent and dependent variable. However, one of ordinary skill in the art would recognize that machine learning training involves inputs (dependent variables) and output (independent variables).
It would have been obvious to one of ordinary skill in the art at the time of Applicant’s filing to modify the combined references with the knowledge available to such an artisan that training involves using dependent and independent variables. This would have been known work in the field of endeavor prompting variations of it in the same field based on use of training models and would provide predictable results.
Regarding claim 12
The one or more instances of non-transitory computer-readable media of claim 10 wherein the addressee service principal operates in a distinguished service domain, the method further comprising:
accessing a plurality of sample textual messages each intended for an addressee service principal in the distinguished service domain;
Jain et al. teaches:
Responses to requests include previous resolutions (plurality of sample textual messages)…
“The techniques and embodiments described herein provide for intelligently and automatically supporting electronic communication requests (also “requests” or “support requests” herein), such as but not limited to, electronically mailed (“emailed”) support requests, technical support requests, postings on messaging threads or forums such as those hosted by websites, social media postings, instant messages, conversations with automated mechanisms such as “bots,” billing, feedback, notifications, etc., that include requests such as for support, information, user access, and/or the like. That is, while embodiments herein may be described in the context of “support requests” as illustrative examples, such embodiments are also contemplated for other types of “requests,” such as but without limitation, the types noted above. In embodiments, requests may be intelligently and automatically routed to correct feature owners (i.e., recipients) of support teams, and intelligently generated automatic responses to requests may be provided to senders of support requests. Responses to requests may include information related to previous resolutions of prior support requests, as well as the prior support requests themselves…” [0031]
See Sample below.
for each of the plurality of sample textual messages:
accessing a category among the plurality of textual message categories manually assigned to the sample textual message;
Example of each service may have support teams or groups (manual) to route and support (categorize) information…
“The embodiments described herein provide for several techniques for properly and automatically routing support requests, and responding to support requests with technical support information, in an intelligent manner. Such techniques allow for scaling to large numbers of services, handling unstructured user inputs, and making accurate routing decisions based on limited information. For instance, to scale to large numbers of services, a communication support system may be configured to provide tracking workflows for hundreds to thousands of services where each service in turn may have several associated support teams or groups. In embodiments, for users or automated mechanisms creating and providing support requests via communication clients, the communication support system is configured to overcome difficulties in providing support requests to correct owners/recipients for support of features/products/systems/services. Because there may not be enough support request information available to manually identify the correct issue owner/team by the sender (e.g., a user notes specific system or service performance feature problems, but the underlying root cause could have been a problem in network, storage, other broad sets of services, etc.), the described techniques and embodiments provide an architecture configured to automatically accomplish such a task based on machine-learning algorithms that consume feature vectors for provided information.” [0034]
See Sample below.
constructing a training observation in which the sample textual message is an independent variable and the category manually assigned to the sample textual message is a dependent variable; and
Model training for classifying support requests (output, therefore, independent variable), where support request vector is an input (dependent variable)…
“Model 222 may be trained by model trainer 220, according to embodiments. Model 222 may be a classification model utilized for classifying electronic communication support requests and/or the like, for proper routing to recipients (e.g., support groups/teams/engineers) for handling. Model 222 may be configured to take a feature vector for a support request as an input from featurizer 210, and provide a model output. Model 222 may generate this model output through a classification of the support request, based on the feature vector, into one or more predefined taxonomies determined during the training of model 222. In embodiments, model 222 may also take sender-specific information as an input to personalize the response to the sender. For example, prior model outputs for communications from the sender, the communications themselves, prior recipients determined for past communications from the sender, and/or the like, may be taken into account by model 222 to personalize model outputs accordingly, resulting in personalized responses to the sender. In some embodiments, users may have specific, personalized model instances of model 222 trained according to one or more sender-specific information inputs and/or user-specific aspects of model 222 may be weighted more as inputs. Personalization may be based on one or more of the follow illustrative examples, although additional bases for personalization may be used as would become apparent of one of skill in the relevant art(s) having the benefit of this disclosure.” [0050]
See Independent and Dependent below.
training the machine learning classification model using the constructed training observations.
Where the classification model is trained…
“Model 222 may be trained by model trainer 220, according to embodiments. Model 222 may be a classification model utilized for classifying electronic communication support requests and/or the like, for proper routing to recipients (e.g., support groups/teams/engineers) for handling. Model 222 may be configured to take a feature vector for a support request as an input from featurizer 210, and provide a model output. Model 222 may generate this model output through a classification of the support request, based on the feature vector, into one or more predefined taxonomies determined during the training of model 222. In embodiments, model 222 may also take sender-specific information as an input to personalize the response to the sender. For example, prior model outputs for communications from the sender, the communications themselves, prior recipients determined for past communications from the sender, and/or the like, may be taken into account by model 222 to personalize model outputs accordingly, resulting in personalized responses to the sender. In some embodiments, users may have specific, personalized model instances of model 222 trained according to one or more sender-specific information inputs and/or user-specific aspects of model 222 may be weighted more as inputs. Personalization may be based on one or more of the follow illustrative examples, although additional bases for personalization may be used as would become apparent of one of skill in the relevant art(s) having the benefit of this disclosure.” [0050]
Sample
The combined references teach training on messages. They do no literally teach sample.
Sivaswamy et al. also in the business of training on messages teaches:
“… Further, network 400 may be evaluated to quantify the performance of evaluating a dataset, such as by use of an evaluation metric (e.g., mean squared error, cross-entropy cost function, accuracy functions, confusion matrix, precision-recall curve, mean absolute error, etc.). Training of network 400, may be performed until a particular predefined accuracy threshold is met. For example, a number of epochs may need to be adjusted to ensure and validate an accuracy that is over 90%. The validation may also be performed by a user to ensure one or more of the following: removal of samples from over-represented classes (alternatively, an under-sampling technique); and adding more samples from under-represented classes (alternatively, an over-sampling technique)” [0085]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to sample messages as taught by Sivaswamy et al. 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. Further motivation is provided by Sivaswamy et al. who teaches sampling for accuracy. Jain benefits as they also teach training models based on messages for proper classification of information. Jain benefits by accurately classifying received messages.
Independent and Dependent
The combined references teach input and output and training. They do not teach independent and dependent variable.
Sivaswamy et al. also in the business of training on messages teaches:
Context of words and categories (dependent variable) dependent on messages (therefore, independent variables)…
“In some embodiments, the context of a word may be dependent on one or more previously analyzed electronic documents (e.g., messages written by users). Examples of parts of speech that may be assigned to words include, but are not limited to, nouns, verbs, adjectives, adverbs, and the like. Examples of other part of speech categories that POS tagger may assign include, but are not limited to, comparative or superlative adverbs, wh-adverbs, conjunctions, determiners, negative particles, possessive markers, prepositions, wh-pronouns, and the like. In some embodiments, the POS tagger may tag or otherwise annotate tokens of a passage with part of speech categories. In some embodiments, the POS tagger may tag tokens or words of a passage to be parsed by natural language processing.” [0065]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to consider dependent and independent values as taught by Sivaswamy et al. 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. Further motivation is provided by Jain who uses machine learning, where inherent with machine learning is training and use of messages (independent values) to determine classification/categories (dependent values).
The combined references teach machine learning and training. They do not literally teach independent and dependent variable. However, one of ordinary skill in the art would recognize that machine learning training involves inputs (dependent variables) and output (independent variables).
It would have been obvious to one of ordinary skill in the art at the time of Applicant’s filing to modify the combined references with the knowledge available to such an artisan that training involves using dependent and independent variables. This would have been known work in the field of endeavor prompting variations of it in the same field based on use of training models and would provide predictable results.
Claims 6 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over the combined references in section (10) above in further view of Patent No. US 10811139 to Wang et al.
Regarding claim 6
The method of claim 1 wherein performing the textual message disposition strategy comprises triggering a reimbursable service assignment performed by the addressee service principal.
Reimbursable
The combined references teach messages. They also teach providing responses to users. They do not teach reimbursable service assignment.
Wang et al. also in the business of messages teaches:
Receiving service requests and providing a response…
“The process may further include receiving service requests for predictions relating to a specified clinical outcome 607. Next, cased on the client request, a selection of the appropriate model from which to generate the requested prediction response may be made 608. With the appropriate model selected, the next step may include refresh/generate requested predictions for the clinical outcomes 609.” (col. 15, lines 63-67 to col. 16, lines 1-2)
Example of response including estimates of missing costs related to incomplete insurance claims (therefore, a reimbursable service)…
“In one embodiment, the web applications (i.e., prediction applications), the prediction module, and the learning module may reside in the cloud and be administered by the invention's operator. Web applications may be structured for specific users, classes of users, templates or related data and predictions. In the healthcare industry, web applications may seek prediction reports for outcomes of specific patient episodes, and focus on factors such as patient guidance, automated healthcare performance benchmarks, estimates of missing clinical episode costs related to incomplete health insurance claims, patient risk scoring, forecasting episode and annual costs, facilities rating systems adjusted for risk, and other points.” (col. 7, lines 60-67 to col. 8, lines 1-5)
“Program rules may be the rules of payor programs for treatment, such as Medicaid and Medicare rules, and of private payor programs by insurance companies or other payors, or programs that combine medical service with payment of costs, such as the federal Veterans Administration or combined private programs. The rules may be for specific treatment episode types (such as, e.g., a knee replacement or a coronary bypass), and may control factors such as the specific content of care, what drugs to use, duration of in-patient care, costs, cost caps, and bonuses or penalties for the service providers pursuant to program rules for certain performance parameters.” (col. 17, lines 56)
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to provide reimbursable costs such as insurance claims as taught by Wang et al. 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. Further motivation is provided by Wang et al. who teaches insurance as a consideration for health care. The combined references benefit as they are directed to providing guidance and support to their service requests.
Regarding claim 17
The one or more instances of non-transitory computer-readable media of claim 13 wherein performing the textual message disposition strategy comprises triggering a reimbursable service assignment performed by the addressee service principal.
Reimbursable
The combined references teach messages. They also teach providing responses to users. They do not teach reimbursable service assignment.
Wang et al. also in the business of messages teaches:
Receiving service requests and providing a response…
“The process may further include receiving service requests for predictions relating to a specified clinical outcome 607. Next, cased on the client request, a selection of the appropriate model from which to generate the requested prediction response may be made 608. With the appropriate model selected, the next step may include refresh/generate requested predictions for the clinical outcomes 609.” (col. 15, lines 63-67 to col. 16, lines 1-2)
Example of response including estimates of missing costs related to incomplete insurance claims (therefore, a reimbursable service)…
“In one embodiment, the web applications (i.e., prediction applications), the prediction module, and the learning module may reside in the cloud and be administered by the invention's operator. Web applications may be structured for specific users, classes of users, templates or related data and predictions. In the healthcare industry, web applications may seek prediction reports for outcomes of specific patient episodes, and focus on factors such as patient guidance, automated healthcare performance benchmarks, estimates of missing clinical episode costs related to incomplete health insurance claims, patient risk scoring, forecasting episode and annual costs, facilities rating systems adjusted for risk, and other points.” (col. 7, lines 60-67 to col. 8, lines 1-5)
“Program rules may be the rules of payor programs for treatment, such as Medicaid and Medicare rules, and of private payor programs by insurance companies or other payors, or programs that combine medical service with payment of costs, such as the federal Veterans Administration or combined private programs. The rules may be for specific treatment episode types (such as, e.g., a knee replacement or a coronary bypass), and may control factors such as the specific content of care, what drugs to use, duration of in-patient care, costs, cost caps, and bonuses or penalties for the service providers pursuant to program rules for certain performance parameters.” (col. 17, lines 56)
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to provide reimbursable costs such as insurance claims as taught by Wang et al. 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. Further motivation is provided by Wang et al. who teaches insurance as a consideration for health care. The combined references benefit as they are directed to providing guidance and support to their service requests.
Claims 7 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over the combined references in section (10) above in further view of Pub. No. US 2021/0224818 to Choudhary et al. in view of Pub. No. US 2018/0285775 to Bergen.
Regarding claim 7
The method of claim 1 wherein performing the textual message disposition strategy comprises:
causing to be displayed:
an advisory that the textual messaging mode is ill-suited to textual messages of the determined textual message category, and a control for canceling the message;
[No Patentable Weight is given to non-functional descriptive claim language of “an advisory that the textual messaging mode is poorly-suited to textual messages…”]
Jain et al. teaches:
Request information as text inputs (textual message)…
“The described embodiments and techniques may perform intelligent and automatic supporting electronic communication requests based on structure that is applied for unstructured inputs provided from users/senders in support requests. That is, unstructured, free form text inputs provided by a user for the request information/data (e.g., title, detailed description, error messages, logs, images, attachments, and/or the like), requires significant time to be consumed to manually read the large volume of text, particularly when support engineers do not have sufficient insights into all possible services/teams to which the support request should be assigned. The embodiments herein provide for communication support systems configured to featurize unstructured text, thus providing structure, for the application of the machine-learning algorithms described.” [0035]
See Ill-Suited below.
receiving user input activating the control; and
See Discard below.
in response to receiving the user input activating the control, discarding the defined message without delivering it to any person.
See Discard below.
Ill-Suited
The combined references teach request messages. They do not teach poorly suited messages.
Choudhary et al. also in the business of request messages teaches:
Requests with ambiguities (poorly suited) for interpreting…
“These problems and others related to the conventional approach provide an incentive for companies to seek ways of partially or fully automating the evaluation and routing processes used for customer support requests. However, in many cases routing cannot be automated effectively with a set of rules, because as noted, there is often no clear pattern or consistency to how a user may phrase their request for assistance, nor is there an effective way to remove ambiguities that arise in interpreting user requests. For example, “I want my money back” needs to be classified as “refund” and there are no obvious keywords to automate such a rule (such as are used in email filtering rules).” [0010]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to determine poorly suited messages as taught by Choudhary et al. 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. Further motivation is provided by Choudhary who teaches messages may be ambiguous and hard to interpret. The combined references benefit by determining messages that may not be interpreted as this allows for subsequent correction of such messages.
Discard
The combined references teach messages. They do not teach discard.
Bergen also in the business of messages teaches:
Choosing (receiving user input) and delete link (activating control) for deleting a reply…
“It should be understood that the operations of the operational scenario can be configured in different ways. For example, additional processing of user category recommendations may be performed to allow multiple recommendations to be submitted. In such a situation, users can be provided with a webpage that contains links to participate in a forum by reading a posted question, replying, escalating a question, promoting an answer to the knowledge base, and voting. In an embodiment, the original requesting user may indicate that the user likes the classification by choosing the like link. Authorized personnel can vote a reply to be the best answer by choosing the best answer link. Other privileges of authorized personnel may include editing the reply by choosing the edit link and deleting the reply by choosing the delete link. Authorized personnel may also promote a recommendation for use in the machine learning training data set.” [0052]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to discard a message using a link as taught by Bergen 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. Further motivation is provided by Bergen the combined references that teach a problem with ambiguous messages and deleting them allows for removing and providing correct messages.
Regarding claim 18
The one or more instances of non-transitory computer-readable media of claim 13 wherein performing the textual message disposition strategy comprises:
causing to be displayed:
an advisory that the textual messaging mode is poorly-suited to textual messages of the determined textual message category, and a control for canceling the message;
[No Patentable Weight is given to non-functional descriptive claim language of “an advisory that the textual messaging mode is poorly-suited to textual messages…”]
Jain et al. teaches:
Request information as text inputs (textual message)…
“The described embodiments and techniques may perform intelligent and automatic supporting electronic communication requests based on structure that is applied for unstructured inputs provided from users/senders in support requests. That is, unstructured, free form text inputs provided by a user for the request information/data (e.g., title, detailed description, error messages, logs, images, attachments, and/or the like), requires significant time to be consumed to manually read the large volume of text, particularly when support engineers do not have sufficient insights into all possible services/teams to which the support request should be assigned. The embodiments herein provide for communication support systems configured to featurize unstructured text, thus providing structure, for the application of the machine-learning algorithms described.” [0035]
See Poorly below.
receiving user input activating the control; and
See Discard below.
in response to receiving the user input activating the control, discarding the defined message without delivering it to any person.
See Discard below.
Poorly
The combined references teach request messages. They do not teach poorly suited messages.
Choudhary et al. also in the business of request messages teaches:
Requests with ambiguities (poorly suited) for interpreting…
“These problems and others related to the conventional approach provide an incentive for companies to seek ways of partially or fully automating the evaluation and routing processes used for customer support requests. However, in many cases routing cannot be automated effectively with a set of rules, because as noted, there is often no clear pattern or consistency to how a user may phrase their request for assistance, nor is there an effective way to remove ambiguities that arise in interpreting user requests. For example, “I want my money back” needs to be classified as “refund” and there are no obvious keywords to automate such a rule (such as are used in email filtering rules).” [0010]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to determine poorly suited messages as taught by Choudhary et al. 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. Further motivation is provided by Choudhary who teaches messages may be ambiguous and hard to interpret. The combined references benefit by determining messages that may not be interpreted as this allows for subsequent correction of such messages.
Discard
The combined references teach messages. They do not teach discard.
Bergen also in the business of messages teaches:
Choosing (receiving user input) and delete link (activating control) for deleting a reply…
“It should be understood that the operations of the operational scenario can be configured in different ways. For example, additional processing of user category recommendations may be performed to allow multiple recommendations to be submitted. In such a situation, users can be provided with a webpage that contains links to participate in a forum by reading a posted question, replying, escalating a question, promoting an answer to the knowledge base, and voting. In an embodiment, the original requesting user may indicate that the user likes the classification by choosing the like link. Authorized personnel can vote a reply to be the best answer by choosing the best answer link. Other privileges of authorized personnel may include editing the reply by choosing the edit link and deleting the reply by choosing the delete link. Authorized personnel may also promote a recommendation for use in the machine learning training data set.” [0052]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to discard a message using a link as taught by Bergen 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. Further motivation is provided by Bergen the combined references that teach a problem with ambiguous messages and deleting them allows for removing and providing correct messages.
Conclusion
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/KENNETH BARTLEY/Primary Examiner, Art Unit 3684