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 .
Specification
The disclosure is objected to because of the following informalities:
(a) In paragraph [0015], the applications are introduced as "applications 134-138," but the same paragraph subsequently recites "the applications 134-140"; consistent numbering is required.
(b) In paragraph [0016], the recitations "the substrate 112 may provide applications deployed directly in cloud" and "inside backend of the substrate 112" use reference numeral 112, which designates the process insights services manager; the substrate is designated by numeral 114.
(c) In paragraph [0026], the recitation "various apps 234-140" is an apparent error for "apps 234-240."
(d) In paragraph [0026], the recitation "and a substrate SIGS app The process-mining substrate app 234 may also communicate" omits a reference numeral for the substrate SIGS app and lacks terminal punctuation between the two sentences.
(e) In paragraphs [0017], [0027], and [0032], "maybe" is used where "may be" is intended (e.g., "the AI builder 218 maybe exposed"; "a mobile application recommendation maybe, recommendation to create a new power app"; "a website layout recommendation maybe, a recommendation for creating a website").
(f) In paragraph [0024], Table 2, "When a mew month starts" is an apparent error for "When a new month starts."
(g) Paragraph [0037] refers to "one or more operations disclosed in FIG. 7" and paragraph [0040] refers to "the logical connections depicted in FIG. 8"; however, no FIG. 7 or FIG. 8 was filed with the application, and only FIGS. 1-6 are filed. The references to FIG. 7 and FIG. 8 must be deleted or corrected..
Appropriate correction is required.
The applicant is advised to review the specification and the drawings carefully and in their entirety for any additional informalities of the type identified above, as the listing herein is exemplary and not exhaustive. Any corrections must be made without introducing new matter; in particular, the references to nonexistent FIGS. 7 and 8 must be cured by deletion or amendment of the text.
Claim Objections
Claim 2, 9-14, 16, 17, 19, and 20 objected to because of the following informalities:
(a) Claims 2, 9, and 16: the recitation "provides recommendation for optimizing a business process" should read "provides a recommendation for optimizing a business process."
(b) Claims 9-14: the recitation "The one or more physically manufactured computer-readable storage media of manufacture of claim 8" contains the surplus phrase "of manufacture," which should be deleted.
(c) Claim 17: the recitation "wherein the wherein the machine learning model" contains duplicated words; "wherein the" should appear once.
(d) Claim 20: the recitation "wherein the wherein the collaboration networks" contains duplicated words; "wherein the" should appear once.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
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 1-20 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 8, and 15 recite the limitation "the plurality of signals" in the wherein clause of the receiving step ("wherein the plurality of signals are clustered with reference to a relevant user node"). There is insufficient antecedent basis for this limitation in the claims. The claims earlier recite only "a plurality of user signals," and it is unclear whether "the plurality of signals" refers to the recited user signals or to a different set of signals. Claims 2-7, 9-14, and 16-20 are rejected due to their dependency on a rejected base claim.
Claims 2, 6, 9, 13, 16, and 19 recite the limitation "the optimization recommendation" in line 1 of the claims. There is insufficient antecedent basis for this limitation in the claims. Independent claims 1, 8, and 15 recite an "optimization prompt" and a "process automation recommendation" (claims 8 and 15 additionally reciting a "mobile application recommendation" and a "website recommendation," respectively), but no "optimization recommendation." It is unclear whether "the optimization recommendation" refers to the process automation recommendation, to one of the other recited recommendations, or to an additional, unrecited recommendation.
The term "relevant" in claims 1, 8, and 15 "a relevant user node" is a relative term which renders the claims indefinite. The term "relevant" is not defined by the claims, the specification does not provide a standard for ascertaining the requisite degree of relevance or the criteria by which a user node is determined to be "relevant," and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The specification merely repeats the term ([0003], [0031], [0046]-[0048]) and provides an example of clustering data with reference to a particular user ([0020], [0025]) without setting forth any objective measure of relevance. Claims 2-7, 9-14, and 16-20 are rejected due to their dependency on a rejected base claim.
The term "key" in claims 1, 8, and 15 "a plurality of key data" and in claims 5, 12, and 18 ("key phrases, key applications, and key collaborators") is a relative term which renders the claims indefinite. The term "key" is not defined by the claims, the specification does not provide a standard for ascertaining what degree of importance distinguishes "key" data (or key phrases, key applications, or key collaborators) from any other data relating to the relevant user node, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The specification provides only unbounded examples ("key phrases, key applications, key collaborators, and other contextual information such as roles of the users, etc.," [0020]) without any criteria for what qualifies as "key."
Claim 8 recites the limitation "the process automation recommendation" and "the mobile application recommendation" in the modifying step. There is insufficient antecedent basis for these limitations in the claim in all circumstances encompassed by the claim. The prior generating step requires generating only "at least one of" a process automation recommendation and a mobile application recommendation; accordingly, where only one recommendation is generated, the other recommendation recited in the modifying step (e.g., "the process automation recommendation" where only a mobile application recommendation was generated) lacks antecedent basis, and it is unclear whether the modifying step nonetheless requires use of a recommendation that was never generated. The metes and bounds of the modifying step therefore cannot be determined. Claims 9-14 are rejected due to their dependency on a rejected base claim.
Claims 8 and 15 are further indefinite because the recitations "at least one of [A] and [B]" are amenable to two plausible constructions of materially different scope, and the claims are inconsistent in their use of conjunctions among otherwise parallel recitations. Claim 8 recites "generating at least one of a process automation recommendation and a mobile application recommendation" and "modifying at least one of a process . . . and a mobile application . . . ," while claim 15 recites "generating at least one of a process automation recommendation and a website recommendation" but "modifying at least one of a process . . . or a website . . . ." It is unclear whether the "at least one of . . . and" recitations require at least one member selected from the recited alternatives (disjunctive) or at least one of each recited member (conjunctive), and the shift between "and" and "or" within claim 15 and between claims 8 and 15 further obscures whether one or both alternatives are required. Where a claim is amenable to two or more plausible constructions, it is indefinite.
Claim 15 recites the limitation "the one or more processor units" in the recitations "executable by the one or more processor units" and "for executing on the one or more processor units." There is insufficient antecedent basis for this limitation in the claim. The claim earlier recites "one or more processing units," and it is unclear whether the "processor units" are the previously recited processing units or additional, distinct components of the system. Claims 16-20 are rejected due to their dependency on a rejected base claim.
Claim 15 recites the limitation "the website recommendations" in the modifying step ("modifying at least one of a process using the process automation recommendation or a website using the website recommendations"). There is insufficient antecedent basis for this limitation in the claim. The claim earlier recites only "a website recommendation" (singular), and it is unclear whether the modifying step requires the single previously recited recommendation or a plurality of website recommendations not previously recited. Claims 16-20 are rejected due to their dependency on a rejected base claim.
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.
Claim 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
MPEP 2106 (III) sets out steps for evaluating whether a claim is drawn to patent-eligible subject matter. The analysis of claims 1-20, in accordance with these steps, follows.
Step 1 Analysis:
Claims 1-7 are directed to method (processes). Claims 15-20 are directed to a system (machine). Claims 8-14 are directed to a computer program (article of manufacture). Therefore, claims 1-20 fall into one of four statutory categories (i.e., process, machine, article of manufacture).
As to claim 1,
Step 2A Prong 1: this claim recites the following abstract ideas:
wherein the plurality of signals are clustered with reference to a relevant user node; (the limitation describes grouping and sorting the signals according to their relation to a particular user, which is an evaluation and judgment activity that can be performed as a mental process in the human mind.)
extracting a plurality of key data relating to the relevant user node; (the limitation describes identifying and selecting key information relating to a particular user, which is an evaluation and judgment activity that can be performed as a mental process in the human mind.)
generating an optimization prompt using the key data; (the limitation describes composing a prompt from the selected key information, which is a mental process implemented using a pen and paper.)
in response to receiving the optimization prompt, generating a process automation recommendation ...; (the limitation describes devising a recommendation for automating a process based on the prompt information, which is a mental process implemented using a pen and paper.)
Step 2A Prong 2 and 2B: the claim recited the following additional elements:
receiving a plurality of user signals from one or more collaboration networks; (this limitation describes data collection/receiving, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
inputting the optimization prompt into a machine learning model; (this limitation describes data transmission, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
using the machine learning model; (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
modifying a process using the process automation recommendation. (This limitation describes carrying out the recommendation determined by the abstract idea, which amounts to insignificant post-solution activity appended to the judicial exception, and mere instruction to apply the abstract idea using generic machinery, see MPEP 2106.05(g) and MPEP 2106.05(d)(II)(i))
The additional element does not integrate the judicial exception into practical application and does not amount to significantly more than the Judicial exception.
As to claim 8,
Step 2A Prong 1: this claim recites the following abstract ideas:
wherein the plurality of signals are clustered with reference to a relevant user node; (the limitation describes grouping and sorting the signals according to their relation to a particular user, which is an evaluation and judgment activity that can be performed as a mental process in the human mind.)
extracting a plurality of key data relating to the relevant user node; (the limitation describes identifying and selecting key information relating to a particular user, which is an evaluation and judgment activity that can be performed as a mental process in the human mind.)
generating an optimization prompt using the key data; (the limitation describes composing a prompt from the selected key information, which is a mental process implemented using a pen and paper.)
in response to receiving the optimization prompt, generating at least one of a process automation recommendation and a mobile application recommendation ...; (the limitation describes devising a recommendation for automating a process or a recommendation for an application based on the prompt information, which is a mental process implemented using a pen and paper.)
Step 2A Prong 2 and 2B: the claim recited the following additional elements:
One or more physically manufactured computer-readable storage media, encoding computer-executable instructions for executing on a computer system a computer process; (This limitation is directed to mere instruction to store the abstract idea on a generic memory and apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
receiving a plurality of user signals from one or more collaboration networks; (this limitation describes data collection/receiving, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
inputting the optimization prompt into a machine learning model; (this limitation describes data transmission, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
using the machine learning model; (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
modifying at least one of a process using the process automation recommendation and a mobile application using the mobile application recommendation. (This limitation describes carrying out the recommendation determined by the abstract idea, which amounts to insignificant post-solution activity appended to the judicial exception, and mere instruction to apply the abstract idea using generic machinery, see MPEP 2106.05(g) and MPEP 2106.05(d)(II)(i))
The additional element does not integrate the judicial exception into practical application and does not amount to significantly more than the Judicial exception.
As to claim 15,
Step 2A Prong 1: this claim recites the following abstract ideas:
wherein the plurality of signals are clustered with reference to a relevant user node; (the limitation describes grouping and sorting the signals according to their relation to a particular user, which is an evaluation and judgment activity that can be performed as a mental process in the human mind.)
extracting a plurality of key data relating to the relevant user node; (the limitation describes identifying and selecting key information relating to a particular user, which is an evaluation and judgment activity that can be performed as a mental process in the human mind.)
generating an optimization prompt using the key data; (the limitation describes composing a prompt from the selected key information, which is a mental process implemented using a pen and paper.)
in response to receiving the optimization prompt, generating at least one of a process automation recommendation and a website recommendation ...; (the limitation describes devising a recommendation for automating a process or a recommendation for a website based on the prompt information, which is a mental process implemented using a pen and paper.)
Step 2A Prong 2 and 2B: the claim recited the following additional elements:
memory; one or more processing units; and a process mining and collaboration recommendation system stored in the memory and executable by the one or more processor units, the process mining and collaboration recommendation system encoding computer-executable instructions on the memory for executing on the one or more processor units a computer process; (This limitation is directed to mere instruction to store the abstract idea on a generic memory and apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
receiving a plurality of user signals from one or more collaboration networks; (this limitation describes data collection/receiving, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
inputting the optimization prompt into a machine learning model; (this limitation describes data transmission, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
using the machine learning model; (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
modifying at least one of a process using the process automation recommendation or a website using the website recommendations. (This limitation describes carrying out the recommendation determined by the abstract idea, which amounts to insignificant post-solution activity appended to the judicial exception, and mere instruction to apply the abstract idea using generic machinery, see MPEP 2106.05(g) and MPEP 2106.05(d)(II)(i))
The additional element does not integrate the judicial exception into practical application and does not amount to significantly more than the Judicial exception.
As to claims 2, 9, and 16,
Step 2A Prong 1: those claims recite the following abstract ideas:
wherein the optimization recommendation provides recommendation for optimizing a business process for the relevant user node. (the limitation describes the content of the recommendation being generated, which merely specifies the type of recommendation devised and is an evaluation and judgment activity that can be performed as a mental process in the human mind.)
Step 2A Prong 2 and 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B. Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) 1.), failing step 2A prong 2. The claims are ineligible.
As to claims 3 and 10,
Step 2A Prong 1: This claim does not recite an additional abstract idea, but the claims depend on claims 1 and 8.
Step 2A Prong 2 and 2B: those claims recited the following additional elements:
wherein the machine learning model is a large language model (LLM); (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
The additional element does not integrate the judicial exception into practical application and does not amount to significantly more than the Judicial exception.
As to claims 5, 12, and 18,
Step 2A Prong 1: those claims recite the following abstract ideas:
wherein the key data includes at least one of key phrases, key applications, and key collaborators related to the relevant user node. (the limitation describes the content of the information being considered, which merely specifies the type of data evaluated and is an evaluation and judgment activity that can be performed as a mental process in the human mind.)
Step 2A Prong 2 and 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B. Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) 1.), failing step 2A prong 2. The claims are ineligible.
As to claims 6, 13, and 19,
Step 2A Prong 1: This claim does not recite an additional abstract idea, but the claims depend on claims 1, 8, and 15.
Step 2A Prong 2 and 2B: those claims recited the following additional elements:
exposing the optimization recommendation via an application programming interface (API); (this limitation describes data transmission, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
The additional limitation of claims 13 and 19 "wherein the computer process further comprising" (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
The additional element does not integrate the judicial exception into practical application and does not amount to significantly more than the Judicial exception.
As to claims 7, 14, and 20,
Step 2A Prong 1: This claim does not recite an additional abstract idea, but the claims depend on claims 1, 8, and 15.
Step 2A Prong 2 and 2B: those claims recited the following additional elements:
wherein the collaboration networks include at least one of an email application, a file sharing application, an online meeting application, and an online chat application. (This limitation describes an intended use and field of use of the collaboration networks, limiting the abstract idea to the environment of generic email, file sharing, online meeting, and online chat applications, which does not integrate the judicial exception into a practical application, see MPEP 2106.05(h). Further, the limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
The additional element does not integrate the judicial exception into practical application and does not amount to significantly more than the Judicial exception.
As to claim 4,
Step 2A Prong 1: this claim recites the following abstract ideas:
in response to receiving the optimization prompt, generating a mobile application recommendation ...; (the limitation describes devising a recommendation for an application based on the prompt information, which is a mental process implemented using a pen and paper.)
Step 2A Prong 2 and 2B: the claim recited the following additional elements:
using the machine learning model; (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
The additional element does not integrate the judicial exception into practical application and does not amount to significantly more than the Judicial exception.
As to claim 11,
Step 2A Prong 1: this claim recites the following abstract ideas:
in response to receiving the optimization prompt, generating a website application recommendation ...; (the limitation describes devising a recommendation for an application based on the prompt information, which is a mental process implemented using a pen and paper.)
Step 2A Prong 2 and 2B: the claim recited the following additional elements:
using the machine learning model; (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
The additional element does not integrate the judicial exception into practical application and does not amount to significantly more than the Judicial exception.
As to claim 17,
Step 2A Prong 1: This claim does not recite an additional abstract idea, but the claim depends on claim 15.
Step 2A Prong 2 and 2B: the claim recited the following additional elements:
wherein the wherein the machine learning model is a large language model (LLM) that employs generative AI; (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
The additional element does not integrate the judicial exception into practical application and does not amount to significantly more than the Judicial exception.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-3, 5-10, and 12-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over El Hattami et al. (US 20230385026 A1) in view of Wang et al. (US 20230060507 A1).
As to claim 1,El Hattami teaches a method, comprising:
extracting a plurality of key data relating to the relevant user node; (see El Hattami paragraph [0020] "In some embodiments, context 124 includes the following items: 1) application metadata: application properties such as application name, business unit, creator, etc.; 2) flow metadata: flow properties such as title, creation date, creator, etc.", and see El Hattami paragraph [0021] "In various embodiments, context-to-text converter 106 receives context 124 (e.g., in an XML or JSON format) and encodes all the elements of the context except the flow embedding ID into a text format. In various embodiments, all the elements of the context are represented as a list of key-value pairs")
generating an optimization prompt using the key data; (see El Hattami paragraph [0016] "In various embodiments, input aggregator 102 creates input text for text-to-text model 110. . . . In various embodiments, input aggregator 102 determines a flow description based on flow description 120 and the output of flow-to-text converter 104 and combines this with context information that is a text output of context-to-text converter 106. In some embodiments, there is a specified order in which the information is combined because starting with the elements that have more influence on the output of text-to-text model 110 can lead to better results.")
inputting the optimization prompt into a machine learning model; (see El Hattami paragraph [0042] "At 606, machine learning inputs based at least in part on the text description and the context information are provided to a machine learning model to determine an implementation prediction for the desired workflow. In some embodiments, the machine learning model is text-to-text model 110 of FIG. 1.")
in response to receiving the optimization prompt, generating a process automation recommendation using the machine learning model; and (see El Hattami paragraph [0013] "As used herein, a workflow, which can also be called a "computerized workflow", "computerized flow", "automation flow", "action flow", "flow", and so forth, refers to an automatic process (e.g., performed by a programmed computer system) comprised of a sequence of actions.", and see El Hattami paragraph [0023] "In various embodiments, text-to-text model 110 predicts an entire flow or partial flow based at least in part on flow description 120. Text-to-text model 110 may also utilize inputs other than a user-provided flow description depending on builder current state 122 and context 124.")
modifying a process using the process automation recommendation. (see El Hattami paragraph [0043] "At 608, one or more processors are used to automatically implement the implementation prediction as a computerized workflow implementation of at least a portion of the desired workflow. In some embodiments, the implementation prediction is converted from a text format to API calls to a flow builder application.")
El Hattami does not explicitly teaches "receiving a plurality of user signals from one or more collaboration networks", and "wherein the plurality of signals are clustered with reference to a relevant user node"
However, Wang teaches
receiving a plurality of user signals from one or more collaboration networks, (see Wang paragraph [0028] "In the embodiment of the disclosure, the application layer may achieve a part of functions based on an instant messaging (IM). The IM is an instant messaging system that may support one-to-one communication between users and may support communication among a plurality of users (for example, a session group). The user may achieve dialogue, information exchange with other users based on the IM application.", and see Wang paragraph [0030] "The comment system may store subjects, pictures and other information of various comment, and may also store a response relationship between the comments. The response relationship between the comments may be a correlation relationship between a comment sent by a reviewer and messages that are reviewed. The attachment system may store and manage attachments uploaded by multiple people. The attachments may include documents, Excels, images, and other resources that may be collaboratively edited.", and see Wang paragraph [0083] "At S510, task information of different work tasks is acquired from different application systems.")
wherein the plurality of signals are clustered with reference to a relevant user node; (see Wang paragraph [0046] "The work card management module 114 is configured to acquire work card information associated with a user from the system layer 130 and display the work card information to the user through a personal board of the user", and see Wang paragraph [0047] "The work cards of the user and association between the work cards may be displayed in the personal board, and the work cards may be also displayed in a sequence such as, the time sequence and the importance sequence from high to low.", and see Wang paragraph [0051] "the knowledge engine module 123 may mine valuable information from the work through technologies such as content semantic understanding, process mining and user analysis")
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the invention of El Hattami, which generates and automatically implements an automation flow by providing user context information to a machine learning model, to include Wang's receiving of user signals from collaboration networks (instant messaging, comment, and collaborative attachment systems) organized with reference to the relevant user, to achieve unified management of work tasks by breaking the boundary between different application systems (see Wang paragraph [0025]) and to mine valuable information from the user's cross-application work activity, which improves automation and intelligence of the office system and further improves working efficiency while reducing manual participation through robotic process automation (see Wang paragraphs [0051], [0054]). The combination merely applies a known technique to a similar system in the same field of machine-learning-assisted work process automation, yielding the predictable result of a flow-generation system whose model input is derived from richer, user-clustered collaboration data, with a reasonable expectation of success.
As to claim 2, El Hattami as modified by Wang teaches the method of claim 1,
wherein the optimization recommendation provides recommendation for optimizing a business process for the relevant user node. (see El Hattami paragraph [0020] "For example, depending on the creator of the flow, or the business unit, the handling of some cases such as the error handling, logging, or managing approvals can be different. For example, a user or set of users might send an email to an administrator if the flow fails, while others will log an error message and terminate the flow.", and see El Hattami paragraph [0032] "In the example shown, flow 200 is used to automate an information technology management process.", and see El Hattami paragraph [0034] "Furthermore, because this feature is data-centric, it can be adapted to a single user or a group of users without any change in the training procedure and usage.")
As to claim 3, El Hattami as modified by Wang teaches the method of claim 1,
wherein the machine learning model is a large language model (LLM). (see El Hattami paragraph [0023] "In some embodiments, text-to-text model 110 is an LLM that has an Encoder-Decoder architecture. In various embodiments, text-to-text model 110 has been pre-trained on a multi-task mixture of unsupervised and supervised tasks for which each task is converted into a text-to-text format. An example of an LLM model with an Encoder-Decoder architecture is the T5 model.")
As to claim 5, El Hattami as modified by Wang teaches the method of claim 1,
wherein the key data includes at least one of key phrases, key applications, and key collaborators related to the relevant user node. (see El Hattami paragraph [0020] "In some embodiments, context 124 includes the following items: 1) application metadata: application properties such as application name, business unit, creator, etc.; 2) flow metadata: flow properties such as title, creation date, creator, etc.")
As to claim 6, El Hattami as modified by Wang teaches the method of claim 1,
further comprising exposing the optimization recommendation via an application programming interface (API). (see El Hattami paragraph [0015] "In various embodiments, flow builder application 114 includes software that can be interfaced with via an API.", and see El Hattami paragraph [0030] "In the example illustrated, text-to-flow unit 100 does not create the final flows, but rather outputs API calls to flow builder application 114 to complete the conversion of the predicted flow in text format to actual flows. Flow builder application 114 is instructed to create a flow via API calls.")
As to claim 3, El Hattami as modified by Wang teaches the method of claim 1,
El Hattami does not explicitly teach "wherein the collaboration networks include at least one of an email application, a file sharing application, an online meeting application, and an online chat application"
However, Wang teaches
wherein the collaboration networks include at least one of an email application, a file sharing application, an online meeting application, and an online chat application. (see Wang paragraph [0028] "The IM is an instant messaging system that may support one-to-one communication between users and may support communication among a plurality of users (for example, a session group). The user may achieve dialogue, information exchange with other users based on the IM application.", and see Wang paragraph [0030] "The attachment system may store and manage attachments uploaded by multiple people. The attachments may include documents, Excels, images, and other resources that may be collaboratively edited.")
As to claim 8, this is directed to a computer program that corresponds to the method of claim 1, See the rejection for claim 1 above, which also applies to claim 8.
As to claim 9, this is directed to a computer program that corresponds to the method of claim 2, See the rejection for claim 2 above, which also applies to claim 9.
As to claim 10, this is directed to a computer program that corresponds to the method of claim 3, See the rejection for claim 3 above, which also applies to claim 10.
As to claim 12, this is directed to a computer program that corresponds to the method of claim 5, See the rejection for claim 5 above, which also applies to claim 12.
As to claim 13, this is directed to a computer program that corresponds to the method of claim 6, See the rejection for claim 6 above, which also applies to claim 13.
As to claim 14, this is directed to a computer program that corresponds to the method of claim 7, See the rejection for claim 7 above, which also applies to claim 14.
As to claim 15, this is directed to a system that corresponds to the method of claim 1, See the rejection for claim 1 above, which also applies to claim 15. in addition the claim recites the following claim elements
memory; (see El Hattami paragraph [0046] "Processor 702 is coupled bi-directionally with memory 710, which can include a first primary storage, typically a random-access memory (RAM), and a second primary storage area, typically a read-only memory (ROM). As is well known in the art, primary storage can be used as a general storage area and as scratch-pad memory, and can also be used to store input data and processed data. Primary storage can also store programming instructions and data, in the form of data objects and text objects, in addition to other data and instructions for processes operating on processor 702.")
one or more processing units; and (see El Hattami paragraph [0045] "Computer system 700 includes at least one microprocessor subsystem (also referred to as a processor or a central processing unit (CPU)) 702. . . . For example, processor 702 can be implemented by a single-chip processor or by multiple processors. In some embodiments, processor 702 is a general-purpose digital processor that controls the operation of computer system 700.")
a process mining and collaboration recommendation system stored in the memory and executable by the one or more processor units, the process mining and collaboration recommendation system encoding computer-executable instructions on the memory for executing on the one or more processor units a computer process, the computer process comprising: (see El Hattami paragraph [0015] "In some embodiments, flow-to-text unit 100 (including its components) is comprised of computer program instructions that are executed on a general-purpose processor, e.g., a central processing unit (CPU), of a programmed computer system. FIG. 7 illustrates an example of a programmed computer system.", and see El Hattami paragraph [0046] "Also, as is well known in the art, primary storage typically includes basic operating instructions, program code, data, and objects used by the processor 702 to perform its functions (e.g., programmed instructions).")
As to claim 16, this is directed to a system that corresponds to the method of claim 2, See the rejection for claim 2 above, which also applies to claim 16.
As to claim 17, this is directed to a system that corresponds to the method of claim 3, See the rejection for claim 3 above, which also applies to claim 17.
As to claim 18, this is directed to a system that corresponds to the method of claim 5, See the rejection for claim 5 above, which also applies to claim 18.
As to claim 19, this is directed to a system that corresponds to the method of claim 6, See the rejection for claim 6 above, which also applies to claim 19.
As to claim 20, this is directed to a system that corresponds to the method of claim 7, See the rejection for claim 7 above, which also applies to claim 20.
Claim(s) 4, and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over El Hattami et al. (US 20230385026 A1) in view of Wang et al. (US 20230060507 A1) and Nott et al. (US 20230009201 A1).
As to claim 4, El Hattami as modified by Wang teaches the method of claim 1,
El Hattami does not explicitly teach "further comprising: in response to receiving the optimization prompt, generating a mobile application recommendation using the machine learning model."
However, Nott teaches
further comprising: in response to receiving the optimization prompt, generating a mobile application recommendation using the machine learning model. (see Nott paragraph [0013] "The intelligent automation experience includes using robot intelligence (e.g., machine learning and artificial intelligence) to perform background monitoring/analyzing of user activity for purposes of identifying and presenting RPAs to be used by users.", and see Nott paragraph [0014] "If the configuring and tooling robotic process automation method determines that there is a match, then the recommendation engine can suggest to the user that there is an existing automation (e.g., an RPA or the like) that does what the user is doing (e.g., mimics the user activity), along with provide an opportunity for the user to view, select, and/or start the existing automation.", and see Nott paragraph [0022] "The robot 122 (and the unattended robots 174 and the attended robots 178) may be an application, applet, script, or the like that may perform and/or automate one or more workflows", and see Nott paragraph [0027] "The unattended robot 174 and/or the attended robot 178 may run or execute on mobile computing or mobile device environments.")
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the invention of El Hattami as modified by Wang, which generates a process automation recommendation using a machine learning model, to include Nott's machine-learning-based suggestion of an existing automation application that runs on a mobile device environment and matches the user's activity, to provide an intelligent automation experience for user activity that is otherwise not available with conventional script automation (see Nott paragraph [0015]) and to make the user aware of an already-existing application that does what the user is doing, along with the opportunity to view, select, and start it (see Nott paragraph [0014]), yielding the predictable result of the model additionally recommending a mobile application in response to the optimization prompt, with a reasonable expectation of success.
As to claim 11, this is directed to a computer program that corresponds to the method of claim 4, See the rejection for claim 4 above, which also applies to claim 11.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ABDULLAH K ABOUD whose telephone number is (571)272-0025. The examiner can normally be reached Mon-Fri 8am-5pm.
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/ABDULLAH KHALED ABOUD/ Examiner, Art Unit 2121
/Li B. Zhen/ Supervisory Patent Examiner, Art Unit 2121
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