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 .
Status of the Claims
Claims 1-9 and 11 are amended. Claims 1-11 are pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/23/2025 was filed before the mailing of this action. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Applicant's arguments filed 11/17/2025 regarding 35 U.S.C. 101 have been fully considered but they are not persuasive.
Applicant argues that the amended claim limitations do not recite a judicial exception, but instead is an approach to analyze data objects generated within computing systems. Applicant also argues that the data objects being obtained and encoded means that the steps cannot be performed in the human brain but must require the usage of a computer. Examiner disagrees. The invention and claims in light of the specification is drawn towards correlating or associating work items with support items to understand requirements of the user-end entities and accordingly determine necessary directions for development and modifications of the services being offered, and the claim recites limitations that correspond to certain methods of organizing human activity as evidenced by limitations detailing linking first [work] items with second [support] support items the second items being associated with an operational request linked to the one or more service offerings. The claim limitations also correspond to mental processes (observation, evaluation, judgment, opinion), as evidenced by limitations detailing encoding work objects with first [work] items into vectors (and encoding second [support] items), estimating a similarity score based on a quantitative comparative assessment, and based on the similarity score linking the second [support] items to the work items. The claim recites an abstract idea. The previous claim recitation of “work items” and “support items” is not the sole reason for indicating that the claims recite a judicial exception. Applicant has now merely broadened their claims by the amendment, however the claims, as indicated above, still recite a judicial exception. The first and second items when viewed in light of the specification are still work items and support items, just changed the name to state “first” and second” in place of “work” and support”. Regarding the mental process argument, claims can recite a mental process even if they are claimed as being performed on a computer. If the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept, the claim is considered to recite a mental process (MPEP §2106.04(a)(2)(III)). Applicant argues that their claims are similar to that of McRO, Inc. dba Planet Blue v. Bandai Namco Games American Inc., 120 USPQ2d 1091 (Fed. Cir. 2016) ("McRO"). Examiner disagrees. Applicant’s claims are in no way analogous to McRO. Applicant’s assertion or reasoning that McRO was eligible because the claims recite a specific invention is grossly misunderstood. The basis for the court’s decision was that the claims improved a computer-related technology by enabling the computer to perform functions that previously could not be performed by a computer and that required the subjective judgement of a human. The court emphasized both the specific claiming of the rules and the specification’s explanation of how the claimed rules enabled the automation of these specific animation tasks that previously could not be automated. This enabling of functionality that could not previously be performed by a computer was what amounted to the improvement in computer-related technology, not the simple recitation of a set of particular rules. In this case, applicant’s claims are ineligible. The judicial exception is not integrated into a practical application because the claim recites the additional elements of computer components or environments recited at a high-level of generality performing the limitations that correspond to the judicial exception. The combination of the additional element amounts to no more than mere instructions to apply the exception using a generic computer. Accordingly, in combination, the additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception using a generic computer. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Thus, when viewed as an ordered combination, nothing in the claim adds significantly more (i.e. an inventive concept) to the abstract idea. The claim is not patent eligible.
The 35 U.S.C. 101 rejection is maintained.
Applicant's arguments filed 11/17/2025 regarding 35 U.S.C. 102 have been fully considered but they are not persuasive.
Applicant argues that the reference, Hamid, does not disclose the amended limitations “…obtain one or more first data objects from a first computing environment associated with software development of one or more computer-based products or platforms…” and “the plurality of items generated by development of the one or more computer-based products or platforms” and further provides that Hamid does not provide any disclosure of teaching for software development and therefore cannot disclose data objects from the software development nor linking of the objects to other objects from end users as claimed. Examiner disagrees. Hamid already discloses the data objects and the linking, and Hamid also discloses that the platform on which these objects are generated and linked is a collaboration platform such as documentation systems, issue tracking systems, project management platforms, and the like (¶0038). Hamid further discloses in ¶0054 the use by the user of the platform automatically generating content created by user while operating a software platform. Hamid ¶0057 discloses that the automatically generated content can be used in multi-platform computing environments. Regarding the computing environment and platform associated with software development of the platform (which is computer-based as evidenced by the processor, etc.), ¶0134 discloses a documentation system may define an environment in which users of the documentation system can leverage a user interface of a frontend of the system to generate documentation in respect of a project, product, process, or goal. For example, a software development team may use a documentation system to document features and functionality of the software product. In other cases, the development team may use the documentation system to capture meeting notes, track project goals, and outline internal best practices. Hamid ¶0144 further discloses the phrases “tenant-owned content” and “platform-specific content” may be used to refer to any and all content, data, metadata, or other information regardless of form or format that is authored, developed, created, or otherwise added by, edited by, or otherwise provided for the benefit of, a user or tenant of a multitenant software platform. Hamid ¶0152 discloses that it may be possible to leverage multiple collaboration platforms to advance individual projects or goals. For example, for a single software development project, a software development team may use (1) a code repository to store project code, executables, and/or static assets, (2) a documentation platform to maintain documentation related to the software development project. Additionally, Hamid ¶0171 and ¶0177 provides further support that the computing environment is associated with software development of one or more computer-based products or platforms, and data objects are obtained from environments having end users for the one or more computer-based products or platforms.
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 1-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
Claims 1-4 recite a system (i.e. machine), claims 5-8 recite a method (i.e. process), and claims 9-11 recite non-transitory computer-readable medium (i.e. machine or article of manufacture). Therefore claims 1-11 fall within one of the four statutory categories of invention.
Independent claim 1 recites obtain one or more first data objects from a first computing environment associated with software development of one or more computer-based products or platforms, each of the one or more first data objects linked with a corresponding first item from amongst a plurality of first items, the plurality of first items generated by development of the one or more [computer-based products or platforms], wherein each of the one or more first data objects comprise at least one descriptor variable associated with the corresponding first item; encode the one or more first data objects associated with each of the plurality of first items into first vectors; obtain one or more second data objects from a second computing environment having end users for the one or more computer-based products or platforms, each linked with a corresponding second item from amongst a plurality of second items, the plurality of second items being associated with an operational request linked to the one or more [computer-based products or platforms], wherein each of the one or more second data objects comprise at least one another descriptor variable associated with the corresponding second item; encode the one or more second data objects associated with each of the plurality of second items into second vectors; estimate a similarity score based on a quantitative comparative assessment of the first vectors and the second vectors, wherein the similarity score is indicative of a contextual similarity between each of the one or more first data objects and each of the one or more second data objects; and link, based on the similarity score, the one or more second items, from among the plurality of second items, with the one or more first items, from among the plurality of first items. The invention and claims in light of the specification is drawn towards correlating or associating work items with support items to understand requirements of the user-end entities and accordingly determine necessary directions for development and modifications of the services being offered, and the claim recites limitations that correspond to certain methods of organizing human activity as evidenced by limitations detailing linking first [work] items with second [support] support items the second items being associated with an operational request linked to the one or more service offerings. The claim limitations also correspond to mental processes (observation, evaluation, judgment, opinion), as evidenced by limitations detailing encoding work objects with first [work] items into vectors (and encoding second [support] items), estimating a similarity score based on a quantitative comparative assessment, and based on the similarity score linking the second [support] items to the work items. The claim recites an abstract idea.
Note: the features or elements in brackets in the above Step 2A Prong One section are inserted for reading clarity, but are analyzed as “additional elements” under Step 2A Prong Two and Step 2B below.
The judicial exception is not integrated into a practical application because the claim recites the additional elements of: a processor and one or more computer-based products or platforms. The additional elements are computer components or environments recited at a high-level of generality performing the above-mentioned limitations. The combination of the additional element amounts to no more than mere instructions to apply the exception using a generic computer. Accordingly, in combination, the additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount 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. Thus, when viewed as an ordered combination, nothing in the claim adds significantly more (i.e. an inventive concept) to the abstract idea. The claim is not patent eligible.
Independent claim 5 recites the limitations of accessing one or more first data objects from a first computing environment associated with software development of one or more computer-based products or platforms, each of the one or more first data objects linked with a corresponding first item from amongst a plurality of first items, the plurality of first items generated by development of the one or more [computer-based products or platforms], wherein each of the one or more first data objects comprise at least one descriptor variable associated with the corresponding first item; encoding the one or more first data objects associated with each of the plurality of first items into first vectors; accessing one or more second data objects from a second computing environment having end users for the one or more [computer-based products or platforms], each linked with a corresponding second item from amongst a plurality of second items, the plurality of second items being associated with an operational request linked to the one or more [computer-based products or platforms], wherein each of the one or more second data objects comprise at least one another descriptor variable associated with the corresponding second item; encoding the one or more second data objects associated with each of the plurality of second items into second vectors; computing a similarity score based on a quantitative comparative assessment of the first vectors and the second vectors, wherein the similarity score is indicative of a contextual similarity between each of the one or more first data objects and each of the one or more second data objects; and generating, based on the similarity score, a recommendation indicating to link each of the one or more second items, from amongst the plurality of second items, with the one or more first items, from amongst the plurality of first items. The invention and claims in light of the specification is drawn towards correlating or associating work items with support items to understand requirements of the user-end entities and accordingly determine necessary directions for development and modifications of the services being offered, and the claim recites limitations that correspond to certain methods of organizing human activity as evidenced by limitations detailing linking first [work] items with second [support] support items the second items being associated with an operational request linked to the one or more service offerings. The claim limitations also correspond to mental processes (observation, evaluation, judgment, opinion), as evidenced by limitations detailing encoding work objects with first [work] items into vectors (and encoding second [support] items), estimating a similarity score based on a quantitative comparative assessment, and based on the similarity score linking the second [support] items to the work items. The claim recites an abstract idea.
Note: the features or elements in brackets in the above Step 2A Prong One section are inserted for reading clarity, but are analyzed as “additional elements” under Step 2A Prong Two and Step 2B below.
The judicial exception is not integrated into a practical application because the claim recites the additional elements of: one or more computer-based products or platforms. The additional element is a computer component or environment recited at a high-level of generality performing the above-mentioned limitations. The combination of the additional element amounts to no more than mere instructions to apply the exception using a generic computer. Accordingly, in combination, the additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element amounts to no more than mere instructions to apply the exception using a generic computer. Mere instructions to apply an exception using a generic computer cannot provide an inventive concept. Thus, when viewed as an ordered combination, nothing in the claim adds significantly more (i.e. an inventive concept) to the abstract idea. The claim is not patent eligible.
Independent claim 9 recites the limitations: receive one or more first data objects from a first computing environment associated with software development of one or more computer-based products or platforms, each of the one or more first data objects linked with a corresponding first item from amongst a plurality of first items, the plurality of first items generated by development of the one or more [computer-based products or platforms], wherein each of the one or more first data objects comprise at least one descriptor variable associated with the corresponding first item; encode the one or more first data objects associated with each of the plurality of first items into first vectors; receive one or more second data objects from a second computing environment having end users for the one or more [computer-based products or platforms], each linked with a corresponding second item from amongst a plurality of second items, the plurality of second items being associated with an operational request linked to the one or more [computer-based products or platforms], wherein each of the one or more second data objects comprise at least one another descriptor variable associated with the corresponding second item; encode the one or more second data objects associated with each of the plurality of second items into second vectors; compute a similarity score based on a quantitative comparative assessment of the first vectors and the second vectors, wherein the similarity score is indicative of a semantic similarity between each of the one or more first data objects and each of the one or more second data objects; determine, based on the similarity score, to recommend linking of the one or more second items, from among the plurality of second items, and the one or more first items, from among the plurality of first items; determine at least one reason for linking the one or more support items with the one or more first items; and generate a recommendation to indicate the at least one reason and the linking of the one or more support items and the one or first work items.
Note: the features or elements in brackets in the above Step 2A Prong One section are inserted for reading clarity, but are analyzed as “additional elements” under Step 2A Prong Two and Step 2B below.
The judicial exception is not integrated into a practical application because the claim recites the additional elements of: a non-transitory computer-readable medium, a processing resource, and computer-based products or platforms. The additional elements are computer components recited at a high-level of generality performing the above-mentioned limitations. The combination of the additional elements amount to no more than mere instructions to apply the exception using a generic computer. Accordingly, in combination, the additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount 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. Thus, when viewed as an ordered combination, nothing in the claim adds significantly more (i.e. an inventive concept) to the abstract idea. The claim is not patent eligible.
Dependent claims 2-4, 6-8, 10, and 11 recite additional limitations that are further directed to the abstract idea analyzed in the rejected claims above. The claims also recite additional elements that have been analyzed in the rejected claims above. Thus, claims 2-4, 6-8, 10, and 11 are also rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-11 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Hamid (2025/0217371).
Claim 1: A system comprising:
a processor to: (Hamid ¶0340 disclosing a processing unit which may be a microprocessor, CPU, etc.)
obtain one or more first data objects from a first computing environment associated with software development of one or more computer-based products or platforms, each of the one or more first data objects linked with a corresponding first item from amongst a plurality of first items, the plurality of first items generated by development of the one or more computer-based products or platforms, wherein each of the one or more first data objects comprise at least one descriptor variable associated with the corresponding first item; (Hamid ¶0054 discloses the use by the user of the platform automatically generating content created by user while operating a software platform; ¶0057 automatically generated content can be used in multi-platform computing environments; ¶0134 discloses a documentation system may define an environment in which users of the documentation system can leverage a user interface of a frontend of the system to generate documentation in respect of a project, product, process, or goal. For example, a software development team may use a documentation system to document features and functionality of the software product. In other cases, the development team may use the documentation system to capture meeting notes, track project goals, and outline internal best practices; ¶0144 the phrases “tenant-owned content” and “platform-specific content” may be used to refer to any and all content, data, metadata, or other information regardless of form or format that is authored, developed, created, or otherwise added by, edited by, or otherwise provided for the benefit of, a user or tenant of a multitenant software platform; ¶0152 it may be possible to leverage multiple collaboration platforms to advance individual projects or goals. For example, for a single software development project, a software development team may use (1) a code repository to store project code, executables, and/or static assets, (2) a documentation platform to maintain documentation related to the software development project; see also ¶0171, ¶0177; ¶0039 disclosing the generative content interface is configured to receive user input including natural language text that may include a natural language question, search string, or natural language query request; see also ¶0041 a user query of “new laptop” may return knowledge base documents related to “new laptop setup operations,” even though the user is actually trying to initiate a request for a new laptop (e.g., via a form or workflow)…in order to improve the quality of the responses and the overall user experience of the generative content service, the generative content service may perform an intent analysis on the user query (e.g., using natural language processing techniques) to identify the intent of the user; ¶0046 disclosing forms may be used to generate requests, send messages, complete workflows, or the like; a user may input a query to a generative content interface in order to request vacation leave, which is a request that may be achieved in the context of a content collaboration platform by completion and/or submission of a form; generative content service may also automatically submit a form or generate an issue ticket using the form; forms may be provided for requesting software updates or changes, creating work tasks, creating bug reports; ¶0049 disclosing a user may select a request-type identifier for the issue record; a request-type identifier may uniquely identify the type (descriptor) of request associated with the issue record, and may be associated with a particular workflow or manner of completing the associated issue; ¶0218 disclosing the request classifiers (descriptor) may include a first request classifier associated with a request for an action, a second request classifier associated with a request for information, and a third request classifier associated with a request for a contact)
encode the one or more first data objects associated with each of the plurality of first items into first vectors; (Hamid ¶0226 disclosing a statement of the user's intent is not separately determined, and instead the natural language input is processed to produce a search vector or feature set (e.g., using string normalization, keyword extraction, lemmatization, etc.), and the search vector or feature set is compared against candidate vectors or feature sets associated with the response classifiers (which may themselves be stored or represented in a format that can be compared to the search vector or feature set))
obtain one or more second data objects from a second computing environment having end users for the one or more computer-based products or platforms, each linked with a corresponding second item from amongst a plurality of second items, the plurality of second items being associated with an operational request linked to the one or more computer-based products or platforms, wherein each of the one or more second data objects comprise at least one another descriptor variable associated with the corresponding second item; (Hamid ¶0054 discloses the use by the user of the platform automatically generating content created by user while operating a software platform; ¶0057 automatically generated content can be used in multi-platform computing environments; ¶0134 discloses a documentation system may define an environment in which users of the documentation system can leverage a user interface of a frontend of the system to generate documentation in respect of a project, product, process, or goal. For example, a software development team may use a documentation system to document features and functionality of the software product. In other cases, the development team may use the documentation system to capture meeting notes, track project goals, and outline internal best practices; ¶0144 the phrases “tenant-owned content” and “platform-specific content” may be used to refer to any and all content, data, metadata, or other information regardless of form or format that is authored, developed, created, or otherwise added by, edited by, or otherwise provided for the benefit of, a user or tenant of a multitenant software platform; ¶0152 it may be possible to leverage multiple collaboration platforms to advance individual projects or goals. For example, for a single software development project, a software development team may use (1) a code repository to store project code, executables, and/or static assets, (2) a documentation platform to maintain documentation related to the software development project; see also ¶0171, ¶0177, and Fig. 4B ; ¶0039 disclosing in response to a user input, the graphical user interface may include search results, links to suggested content and, in some instances, a link to a form or email that can be used to provide additional operations; response may also include a pre-populated form or email, which may be prepopulated based on a chat history or other natural language inputs provided by the user; response may also include follow-up questions that are tailored to further completing a form that is relevant to the user's inquiry; ¶0038 disclosing content items (e.g., pages, knowledge base documents, issues, forms, issue ticket records, source code, and documentation) that can be used to synthesize an automatically generated answer, links to relevant content, and/or summaries of content; ¶0049 disclosing the request-type identifier may also be associated with a particular form or forms in the content collaboration platform that are used to complete the issue; see also ¶0050)
encode the one or more second data objects associated with each of the plurality of second items into second vectors; (Hamid ¶0238 disclosing evaluating the responsiveness of the text snippet portions (or other portions of content items that have been returned by the content stores as candidate results to the query) with respect to the natural language user input or a representative thereof; each text snippet portion may be subjected to an embedding operation and/or generate a multi-dimensional vector representation of the text; the text snippets may be represented as a vector or other multi-dimensional data element allowing for comparison to a similarly vectorized or processed representation of the natural language user input, e.g., a representative vector may be constructed using a word vectorization service that maps words or phrases into a vector of numbers or other characters)
estimate a similarity score based on a quantitative comparative assessment of the first vectors and the second vectors, wherein the similarity score is indicative of a contextual similarity between each of the one or more first data objects and each of the one or more second data objects; and (Hamid ¶0238 disclosing evaluating the responsiveness of the text snippet portions (or other portions of content items that have been returned by the content stores as candidate results to the query) with respect to the natural language user input or a representative thereof; each text snippet portion may be subjected to an embedding operation and/or generate a multi-dimensional vector representation of the text; the text snippets may be represented as a vector or other multi-dimensional data element allowing for comparison to a similarly vectorized or processed representation of the natural language user input, e.g., a representative vector may be constructed using a word vectorization service that maps words or phrases into a vector of numbers or other characters; a comparison of each vector or other representation may be performed with respect to the user input to determine a degree of correlation or similarity; a cosine similarity or other similar comparison is performed between respective vectors and a score or value is determined for each pairing; evaluated snippets may be ranked or sorted by degree of correlation and a subset of snippets may be selected for use in constructing a prompt)
link, based on the similarity score, the one or more second items, from among the plurality of second items, with the one or more first items, from among the plurality of first items. (Hamid ¶0238 disclosing a threshold score or other degree of correlation is used to select the subset of snippets; a threshold number of top scoring results are selected; the top-scoring results that provide a threshold number of characters or aggregated snippet size are selected; see also ¶0239; ¶0221 disclosing determining intent confidence scores for a natural language input with respect to each of a set of request classifiers, the intent analysis module can tailor its search and generative response operations to best satisfy the user's intent; the generative content service may use the intent confidence scores to select or limit the particular content stores that are searched in order to find content items with which to generate a response to the user; generative content service may use intent confidence scores with respect to different request classifiers to tailor its operations in various ways, including selecting a search target data store, response type, response formatting, follow-up options, response appearance, selectable options or links (e.g., what types of selectable options or links are included in a response))
Claim 2: The system of claim 1, wherein the processor is to render at least one of:
a reasoning indicator to indicate at least a reason for linking the one or more second items with the one or more first items; and (Hamid ¶0051 disclosing the link between an issue record and a form being advantageous, e.g., there is a strong likelihood that the associated form will be responsive to the query that gave rise to the issue; if the generative content service identifies an issue record that is similar to a user's query, there can be a high degree of confidence that the form used to resolve the issue will also resolve the user's query; ¶0222 disclosing the score for each response classifier is between 0 and 1 (with higher values representing higher confidence). If the intent confidence score satisfies an intent confidence condition (e.g., a score greater than or equal to 0.7, or any other suitable condition), the generative content service may attempt to satisfy the user's query in a first manner (e.g., searching in a content store associated with that type of user request); ¶0225 disclosing if the statement of the user's intent is more similar to intent statements associated with a first response classifier (e.g., a request for an action) than to those associated with a second response classifier (e.g., a request for information), the intent confidence score for the request for an action will be higher than the request for information; see also ¶0235, ¶0238…a comparison of each vector or other representation may be performed with respect to the user input to determine a degree of correlation or similarity. In one example implementation, a cosine similarity or other similar comparison is performed between respective vectors and a score or value is determined for each pairing…a threshold score or other degree of correlation is used to select the subset of snippets)
a feedback option to receive at least one of a positive feedback and a negative feedback, the positive feedback indicating acceptance of the linking and the negative feedback indicating rejection of the linking. (Hamid ¶0244 disclosing the generative content service may also receive express feedback provided via the interface regarding the suitability or accuracy of the results; generative content service may also provide feedback that results from object selections, dwell time on the generative response, subsequent queries, and other user interaction events that may signal positive or negative feedback, which may be used to train intent recognition modules or other aspects of the system to improve the accuracy and performance of subsequent responses; ¶0247 disclosing generative content service or a related service may receive feedback or user validation from user accounts…in response to receiving a positive feedback from an account flagged as having appropriate subject matter expertise (e.g., associated subject matter expertise has a threshold similarity to the subject matter of the generative response), the service or system may designate the generative response as verified or endorsed; see also Fig. 5A)
Claim 3: The system of claim 2, wherein the processor is to tune subsequent linking, of one or more second items and one or more first items, based on at least one of the positive feedback and the negative feedback. (Hamid ¶0244 disclosing the generative content service may also receive express feedback provided via the interface regarding the suitability or accuracy of the results; generative content service may also provide feedback that results from object selections, dwell time on the generative response, subsequent queries, and other user interaction events that may signal positive or negative feedback, which may be used to train intent recognition modules or other aspects of the system to improve the accuracy and performance of subsequent responses; ¶0247 disclosing generative content service or a related service may receive feedback or user validation from user accounts…in response to receiving a positive feedback from an account flagged as having appropriate subject matter expertise (e.g., associated subject matter expertise has a threshold similarity to the subject matter of the generative response), the service or system may designate the generative response as verified or endorsed; see also Fig. 5A)
Claim 4: The system of claim 1, wherein the first vectors and the second vectors are numerical vectors. (Hamid ¶0238 disclosing a representative vector may be constructed using a word vectorization service that maps words or phrases into a vector of numbers or other characters; comparison of each vector or other representation may be performed with respect to the user input to determine a degree of correlation or similarity; a cosine similarity or other similar comparison is performed between respective vectors and a score or value is determined for each pairing)
Claim 5: A method comprising:
accessing one or more first data objects from a first computing environment associated with software development of one or more computer-based products or platforms, each of the one or more first data objects linked with a corresponding first item from amongst a plurality of first items, the plurality of first items generated by development of the one or more computer-based products or platforms, wherein each of the one or more first data objects comprise at least one descriptor variable associated with the corresponding first item; (Hamid ¶0054 discloses the use by the user of the platform automatically generating content created by user while operating a software platform; ¶0057 automatically generated content can be used in multi-platform computing environments; ¶0134 discloses a documentation system may define an environment in which users of the documentation system can leverage a user interface of a frontend of the system to generate documentation in respect of a project, product, process, or goal. For example, a software development team may use a documentation system to document features and functionality of the software product. In other cases, the development team may use the documentation system to capture meeting notes, track project goals, and outline internal best practices; ¶0144 the phrases “tenant-owned content” and “platform-specific content” may be used to refer to any and all content, data, metadata, or other information regardless of form or format that is authored, developed, created, or otherwise added by, edited by, or otherwise provided for the benefit of, a user or tenant of a multitenant software platform; ¶0152 it may be possible to leverage multiple collaboration platforms to advance individual projects or goals. For example, for a single software development project, a software development team may use (1) a code repository to store project code, executables, and/or static assets, (2) a documentation platform to maintain documentation related to the software development project; see also ¶0171, ¶0177; ¶0039 disclosing the generative content interface is configured to receive user input including natural language text that may include a natural language question, search string, or natural language query request (work items); see also ¶0041 a user query of “new laptop” may return knowledge base documents related to “new laptop setup operations,” (work items) even though the user is actually trying to initiate a request for a new laptop (e.g., via a form or workflow)…in order to improve the quality of the responses and the overall user experience of the generative content service, the generative content service may perform an intent analysis on the user query (e.g., using natural language processing techniques) to identify the intent of the user; ¶0046 disclosing forms may be used to generate requests, send messages, complete workflows, or the like; a user may input a query to a generative content interface in order to request vacation leave, which is a request that may be achieved in the context of a content collaboration platform by completion and/or submission of a form; generative content service may also automatically submit a form or generate an issue ticket using the form; forms may be provided for requesting software updates or changes, creating work tasks, creating bug reports; ¶0049 disclosing a user may select a request-type identifier for the issue record; a request-type identifier may uniquely identify the type of request associated with the issue record, and may be associated with a particular workflow or manner of completing the associated issue; ¶0218 disclosing the request classifiers may include a first request classifier associated with a request for an action, a second request classifier associated with a request for information, and a third request classifier associated with a request for a contact)
encoding the one or more first data objects associated with each of the plurality of first items into first vectors; (Hamid ¶0226 disclosing a statement of the user's intent is not separately determined, and instead the natural language input is processed to produce a search vector or feature set (e.g., using string normalization, keyword extraction, lemmatization, etc.), and the search vector or feature set is compared against candidate vectors or feature sets associated with the response classifiers (which may themselves be stored or represented in a format that can be compared to the search vector or feature set))
accessing one or more second data objects from a second computing environment having end users for the one or more computer-based products or platforms, each linked with a corresponding second item from amongst a plurality of second items, the plurality of second items being associated with an operational request linked to the one or more computer-based products or platforms, wherein each of the one or more second data objects comprise at least one another descriptor variable associated with the corresponding second item; (Hamid ¶0054 discloses the use by the user of the platform automatically generating content created by user while operating a software platform; ¶0057 automatically generated content can be used in multi-platform computing environments; ¶0134 discloses a documentation system may define an environment in which users of the documentation system can leverage a user interface of a frontend of the system to generate documentation in respect of a project, product, process, or goal. For example, a software development team may use a documentation system to document features and functionality of the software product. In other cases, the development team may use the documentation system to capture meeting notes, track project goals, and outline internal best practices; ¶0144 the phrases “tenant-owned content” and “platform-specific content” may be used to refer to any and all content, data, metadata, or other information regardless of form or format that is authored, developed, created, or otherwise added by, edited by, or otherwise provided for the benefit of, a user or tenant of a multitenant software platform; ¶0152 it may be possible to leverage multiple collaboration platforms to advance individual projects or goals. For example, for a single software development project, a software development team may use (1) a code repository to store project code, executables, and/or static assets, (2) a documentation platform to maintain documentation related to the software development project; see also ¶0171, ¶0177, Fig. 4B; ¶0039 disclosing in response to a user input, the graphical user interface may include search results, links to suggested content and, in some instances, a link to a form or email that can be used to provide additional operations (support); response may also include a pre-populated form or email, which may be prepopulated based on a chat history or other natural language inputs provided by the user; response may also include follow-up questions that are tailored to further completing a form that is relevant to the user's inquiry; ¶0038 disclosing content items (e.g., pages, knowledge base documents, issues, forms, issue ticket records, source code, and documentation) that can be used to synthesize an automatically generated answer, links to relevant content, and/or summaries of content; ¶0049 disclosing the request-type identifier may also be associated with a particular form or forms in the content collaboration platform that are used to complete the issue; see also ¶0050)
encoding the one or more second data objects associated with each of the plurality of second items into second vectors; (Hamid ¶0238 disclosing evaluating the responsiveness of the text snippet portions (or other portions of content items that have been returned by the content stores as candidate results to the query) with respect to the natural language user input or a representative thereof; each text snippet portion may be subjected to an embedding operation and/or generate a multi-dimensional vector representation of the text; the text snippets may be represented as a vector or other multi-dimensional data element allowing for comparison to a similarly vectorized or processed representation of the natural language user input, e.g., a representative vector may be constructed using a word vectorization service that maps words or phrases into a vector of numbers or other characters)
computing a similarity score based on a quantitative comparative assessment of the first vectors and the second vectors, wherein the similarity score is indicative of a contextual similarity between each of the one or more first data objects and each of the one or more second data objects; and (Hamid ¶0238 disclosing evaluating the responsiveness of the text snippet portions (or other portions of content items that have been returned by the content stores as candidate results to the query) with respect to the natural language user input or a representative thereof; each text snippet portion may be subjected to an embedding operation and/or generate a multi-dimensional vector representation of the text; the text snippets may be represented as a vector or other multi-dimensional data element allowing for comparison to a similarly vectorized or processed representation of the natural language user input, e.g., a representative vector may be constructed using a word vectorization service that maps words or phrases into a vector of numbers or other characters; a comparison of each vector or other representation may be performed with respect to the user input to determine a degree of correlation or similarity; a cosine similarity or other similar comparison is performed between respective vectors and a score or value is determined for each pairing; evaluated snippets may be ranked or sorted by degree of correlation and a subset of snippets may be selected for use in constructing a prompt)
generating, based on the similarity score, a recommendation indicating to link each of the one or more second items, from amongst the plurality of second items, with the one or more first items, from amongst the plurality of first items. (Hamid ¶0238 disclosing a threshold score or other degree of correlation is used to select the subset of snippets; a threshold number of top scoring results are selected; the top-scoring results that provide a threshold number of characters or aggregated snippet size are selected; see also ¶0239; ¶0221 disclosing determining intent confidence scores for a natural language input with respect to each of a set of request classifiers, the intent analysis module can tailor its search and generative response operations to best satisfy the user's intent; the generative content service may use the intent confidence scores to select or limit the particular content stores that are searched in order to find content items with which to generate a response to the user; generative content service may use intent confidence scores with respect to different request classifiers to tailor its operations in various ways, including selecting a search target data store, response type, response formatting, follow-up options, response appearance, selectable options or links (e.g., what types of selectable options or links are included in a response))
Claim 6: The method of claim 5, the method further comprising:
rendering a reasoning indicator to indicate a reason for recommending linking of each of the one or more second items with the one or more first items; and (Hamid ¶0051 disclosing the link between an issue record and a form being advantageous, e.g., there is a strong likelihood that the associated form will be responsive to the query that gave rise to the issue; if the generative content service identifies an issue record that is similar to a user's query, there can be a high degree of confidence that the form used to resolve the issue will also resolve the user's query; ¶0222 disclosing the score for each response classifier is between 0 and 1 (with higher values representing higher confidence). If the intent confidence score satisfies an intent confidence condition (e.g., a score greater than or equal to 0.7, or any other suitable condition), the generative content service may attempt to satisfy the user's query in a first manner (e.g., searching in a content store associated with that type of user request); ¶0225 disclosing if the statement of the user's intent is more similar to intent statements associated with a first response classifier (e.g., a request for an action) than to those associated with a second response classifier (e.g., a request for information), the intent confidence score for the request for an action will be higher than the request for information; see also ¶0235, ¶0238…a comparison of each vector or other representation may be performed with respect to the user input to determine a degree of correlation or similarity. In one example implementation, a cosine similarity or other similar comparison is performed between respective vectors and a score or value is determined for each pairing…a threshold score or other degree of correlation is used to select the subset of snippets)
rendering a feedback option for receiving at least one of a positive feedback and a negative feedback, the positive feedback indicating acceptance of the recommendation and the negative feedback indicating rejection of the recommendation. (Hamid ¶0244 disclosing the generative content service may also receive express feedback provided via the interface regarding the suitability or accuracy of the results; generative content service may also provide feedback that results from object selections, dwell time on the generative response, subsequent queries, and other user interaction events that may signal positive or negative feedback, which may be used to train intent recognition modules or other aspects of the system to improve the accuracy and performance of subsequent responses; ¶0247 disclosing generative content service or a related service may receive feedback or user validation from user accounts…in response to receiving a positive feedback from an account flagged as having appropriate subject matter expertise (e.g., associated subject matter expertise has a threshold similarity to the subject matter of the generative response), the service or system may designate the generative response as verified or endorsed; see also Fig. 5A; ¶0088 disclosing reinforcement training by human feedback can reinforce high quality, tone neutral, continuations provided by the LLM (e.g., positive feedback corresponds to positive reward) while simultaneously disincentivizing the LLM to produce offensive continuations (e.g., negative feedback corresponds to negative reward; an LLM can be fine-tuned to preferentially produce desirable, inoffensive, generative output; ¶0044 further discloses that if the response confidence analysis model returns a high confidence score for the initial search (e.g., a score that satisfies a confidence condition), the generative content service may provide a response based on those results; however, if the confidence score is low (e.g., does not satisfy the confidence condition), the generative content service may search other platforms or content stores (e.g., ones that had a lower intent confidence score))
Claim 7: The method of claim 6, the method further comprising linking each of the one or more second items with the one or more first items on receiving the positive feedback. (Hamid ¶0238 disclosing a threshold score or other degree of correlation is used to select the subset of snippets; a threshold number of top scoring results are selected; the top-scoring results that provide a threshold number of characters or aggregated snippet size are selected; see also ¶0239; ¶0221 disclosing determining intent confidence scores for a natural language input with respect to each of a set of request classifiers, the intent analysis module can tailor its search and generative response operations to best satisfy the user's intent; the generative content service may use the intent confidence scores to select or limit the particular content stores that are searched in order to find content items with which to generate a response to the user; generative content service may use intent confidence scores with respect to different request classifiers to tailor its operations in various ways, including selecting a search target data store, response type, response formatting, follow-up options, response appearance, selectable options or links (e.g., what types of selectable options or links are included in a response); ¶0244 disclosing the generative content service may also receive express feedback provided via the interface regarding the suitability or accuracy of the results; generative content service may also provide feedback that results from object selections, dwell time on the generative response, subsequent queries, and other user interaction events that may signal positive or negative feedback, which may be used to train intent recognition modules or other aspects of the system to improve the accuracy and performance of subsequent responses; ¶0247 disclosing generative content service or a related service may receive feedback or user validation from user accounts…in response to receiving a positive feedback from an account flagged as having appropriate subject matter expertise (e.g., associated subject matter expertise has a threshold similarity to the subject matter of the generative response), the service or system may designate the generative response as verified or endorsed; see also Fig. 5A)
Claim 8: The method of claim 6, the method further comprising tuning subsequent recommendations, indicating linking of one or more second items and one or more first items, based on the positive feedback and the negative feedback. (Hamid ¶0244 disclosing the generative content service may also receive express feedback provided via the interface regarding the suitability or accuracy of the results; generative content service may also provide feedback that results from object selections, dwell time on the generative response, subsequent queries, and other user interaction events that may signal positive or negative feedback, which may be used to train intent recognition modules or other aspects of the system to improve the accuracy and performance of subsequent responses; ¶0247 disclosing generative content service or a related service may receive feedback or user validation from user accounts…in response to receiving a positive feedback from an account flagged as having appropriate subject matter expertise (e.g., associated subject matter expertise has a threshold similarity to the subject matter of the generative response), the service or system may designate the generative response as verified or endorsed; see also Fig. 5A; ¶0238 disclosing a threshold score or other degree of correlation is used to select the subset of snippets; a threshold number of top scoring results are selected; the top-scoring results that provide a threshold number of characters or aggregated snippet size are selected; see also ¶0239; ¶0221 disclosing determining intent confidence scores for a natural language input with respect to each of a set of request classifiers, the intent analysis module can tailor its search and generative response operations to best satisfy the user's intent; the generative content service may use the intent confidence scores to select or limit the particular content stores that are searched in order to find content items with which to generate a response to the user; generative content service may use intent confidence scores with respect to different request classifiers to tailor its operations in various ways, including selecting a search target data store, response type, response formatting, follow-up options, response appearance, selectable options or links (e.g., what types of selectable options or links are included in a response))
Claim 9: A non-transitory computer-readable medium comprising instructions, the instructions being executable by a processing resource to:
receive one or more first data objects from a first computing environment associated with software development of one or more computer-based products or platforms, each of the one or more first data objects linked with a corresponding first item from amongst a plurality of first items, the plurality of first items generated by development of the one or more computer-based products or platforms, wherein each of the one or more first data objects comprise at least one descriptor variable associated with the corresponding first item; (Hamid ¶0054 discloses the use by the user of the platform automatically generating content created by user while operating a software platform; ¶0057 automatically generated content can be used in multi-platform computing environments; ¶0134 discloses a documentation system may define an environment in which users of the documentation system can leverage a user interface of a frontend of the system to generate documentation in respect of a project, product, process, or goal. For example, a software development team may use a documentation system to document features and functionality of the software product. In other cases, the development team may use the documentation system to capture meeting notes, track project goals, and outline internal best practices; ¶0144 the phrases “tenant-owned content” and “platform-specific content” may be used to refer to any and all content, data, metadata, or other information regardless of form or format that is authored, developed, created, or otherwise added by, edited by, or otherwise provided for the benefit of, a user or tenant of a multitenant software platform; ¶0152 it may be possible to leverage multiple collaboration platforms to advance individual projects or goals. For example, for a single software development project, a software development team may use (1) a code repository to store project code, executables, and/or static assets, (2) a documentation platform to maintain documentation related to the software development project; see also ¶0171, ¶0177; ¶0039 disclosing the generative content interface is configured to receive user input including natural language text that may include a natural language question, search string, or natural language query request (work items); see also ¶0041 a user query of “new laptop” may return knowledge base documents related to “new laptop setup operations,” (work items) even though the user is actually trying to initiate a request for a new laptop (e.g., via a form or workflow)…in order to improve the quality of the responses and the overall user experience of the generative content service, the generative content service may perform an intent analysis on the user query (e.g., using natural language processing techniques) to identify the intent of the user; ¶0046 disclosing forms may be used to generate requests, send messages, complete workflows, or the like; a user may input a query to a generative content interface in order to request vacation leave, which is a request that may be achieved in the context of a content collaboration platform by completion and/or submission of a form; generative content service may also automatically submit a form or generate an issue ticket using the form; forms may be provided for requesting software updates or changes, creating work tasks, creating bug reports; ¶0049 disclosing a user may select a request-type identifier for the issue record; a request-type identifier may uniquely identify the type of request associated with the issue record, and may be associated with a particular workflow or manner of completing the associated issue; ¶0218 disclosing the request classifiers may include a first request classifier associated with a request for an action, a second request classifier associated with a request for information, and a third request classifier associated with a request for a contact)
encode the one or more first data objects associated with each of the plurality of first items into first vectors; (Hamid ¶0226 disclosing a statement of the user's intent is not separately determined, and instead the natural language input is processed to produce a search vector or feature set (e.g., using string normalization, keyword extraction, lemmatization, etc.), and the search vector or feature set is compared against candidate vectors or feature sets associated with the response classifiers (which may themselves be stored or represented in a format that can be compared to the search vector or feature set))
receive one or more second data objects from a second computing environment having end users for the one or more computer-based products or platforms, each linked with a corresponding second item from amongst a plurality of second items, the plurality of second items being associated with an operational request linked to the one or more computer-based products or platforms, wherein each of the one or more second data objects comprise at least one another descriptor variable associated with the corresponding second item; (Hamid ¶0054 discloses the use by the user of the platform automatically generating content created by user while operating a software platform; ¶0057 automatically generated content can be used in multi-platform computing environments; ¶0134 discloses a documentation system may define an environment in which users of the documentation system can leverage a user interface of a frontend of the system to generate documentation in respect of a project, product, process, or goal. For example, a software development team may use a documentation system to document features and functionality of the software product. In other cases, the development team may use the documentation system to capture meeting notes, track project goals, and outline internal best practices; ¶0144 the phrases “tenant-owned content” and “platform-specific content” may be used to refer to any and all content, data, metadata, or other information regardless of form or format that is authored, developed, created, or otherwise added by, edited by, or otherwise provided for the benefit of, a user or tenant of a multitenant software platform; ¶0152 it may be possible to leverage multiple collaboration platforms to advance individual projects or goals. For example, for a single software development project, a software development team may use (1) a code repository to store project code, executables, and/or static assets, (2) a documentation platform to maintain documentation related to the software development project; see also ¶0171, ¶0177, Fig. 4B; ¶0039 disclosing in response to a user input, the graphical user interface may include search results, links to suggested content and, in some instances, a link to a form or email that can be used to provide additional operations (support); response may also include a pre-populated form or email, which may be prepopulated based on a chat history or other natural language inputs provided by the user; response may also include follow-up questions that are tailored to further completing a form that is relevant to the user's inquiry; ¶0038 disclosing content items (e.g., pages, knowledge base documents, issues, forms, issue ticket records, source code, and documentation) that can be used to synthesize an automatically generated answer, links to relevant content, and/or summaries of content; ¶0049 disclosing the request-type identifier may also be associated with a particular form or forms in the content collaboration platform that are used to complete the issue; see also ¶0050)
encode the one or more second data objects associated with each of the plurality of second items into second vectors; (Hamid ¶0238 disclosing evaluating the responsiveness of the text snippet portions (or other portions of content items that have been returned by the content stores as candidate results to the query) with respect to the natural language user input or a representative thereof; each text snippet portion may be subjected to an embedding operation and/or generate a multi-dimensional vector representation of the text; the text snippets may be represented as a vector or other multi-dimensional data element allowing for comparison to a similarly vectorized or processed representation of the natural language user input, e.g., a representative vector may be constructed using a word vectorization service that maps words or phrases into a vector of numbers or other characters)
compute a similarity score based on a quantitative comparative assessment of the first vectors and the second vectors, wherein the similarity score is indicative of a semantic similarity between each of the one or more first data objects and each of the one or more second data objects; (Hamid ¶0238 disclosing evaluating the responsiveness of the text snippet portions (or other portions of content items that have been returned by the content stores as candidate results to the query) with respect to the natural language user input or a representative thereof; each text snippet portion may be subjected to an embedding operation and/or generate a multi-dimensional vector representation of the text; the text snippets may be represented as a vector or other multi-dimensional data element allowing for comparison to a similarly vectorized or processed representation of the natural language user input, e.g., a representative vector may be constructed using a word vectorization service that maps words or phrases into a vector of numbers or other characters; a comparison of each vector or other representation may be performed with respect to the user input to determine a degree of correlation or similarity; a cosine similarity or other similar comparison is performed between respective vectors and a score or value is determined for each pairing; evaluated snippets may be ranked or sorted by degree of correlation and a subset of snippets may be selected for use in constructing a prompt)
determine, based on the similarity score, to recommend linking of the one or more second items, from among the plurality of second items, and the one or more first items, from among the plurality of first items; (Hamid ¶0238 disclosing a threshold score or other degree of correlation is used to select the subset of snippets; a threshold number of top scoring results are selected; the top-scoring results that provide a threshold number of characters or aggregated snippet size are selected; see also ¶0239; ¶0221 disclosing determining intent confidence scores for a natural language input with respect to each of a set of request classifiers, the intent analysis module can tailor its search and generative response operations to best satisfy the user's intent; the generative content service may use the intent confidence scores to select or limit the particular content stores that are searched in order to find content items with which to generate a response to the user; generative content service may use intent confidence scores with respect to different request classifiers to tailor its operations in various ways, including selecting a search target data store, response type, response formatting, follow-up options, response appearance, selectable options or links (e.g., what types of selectable options or links are included in a response))
determine at least one reason for linking the one or more support items with the one or more first items; and generate a recommendation to indicate the at least one reason and the linking of the one or more support items and the one or first work items. (Hamid ¶0051 disclosing the link between an issue record and a form being advantageous, e.g., there is a strong likelihood that the associated form will be responsive to the query that gave rise to the issue; if the generative content service identifies an issue record that is similar to a user's query, there can be a high degree of confidence that the form used to resolve the issue will also resolve the user's query; ¶0222 disclosing the score for each response classifier is between 0 and 1 (with higher values representing higher confidence). If the intent confidence score satisfies an intent confidence condition (e.g., a score greater than or equal to 0.7, or any other suitable condition), the generative content service may attempt to satisfy the user's query in a first manner (e.g., searching in a content store associated with that type of user request); ¶0225 disclosing if the statement of the user's intent is more similar to intent statements associated with a first response classifier (e.g., a request for an action) than to those associated with a second response classifier (e.g., a request for information), the intent confidence score for the request for an action will be higher than the request for information; see also ¶0235, ¶0238…a comparison of each vector or other representation may be performed with respect to the user input to determine a degree of correlation or similarity. In one example implementation, a cosine similarity or other similar comparison is performed between respective vectors and a score or value is determined for each pairing…a threshold score or other degree of correlation is used to select the subset of snippet)
Claim 10: The non-transitory computer-readable medium of claim 9, the instructions being executable by the processing resource to render at least one of:
a reasoning indicator to indicate the at least one reason; ; (Hamid ¶0051 disclosing the link between an issue record and a form being advantageous, e.g., there is a strong likelihood that the associated form will be responsive to the query that gave rise to the issue; if the generative content service identifies an issue record that is similar to a user's query, there can be a high degree of confidence that the form used to resolve the issue will also resolve the user's query; ¶0222 disclosing the score for each response classifier is between 0 and 1 (with higher values representing higher confidence). If the intent confidence score satisfies an intent confidence condition (e.g., a score greater than or equal to 0.7, or any other suitable condition), the generative content service may attempt to satisfy the user's query in a first manner (e.g., searching in a content store associated with that type of user request); ¶0225 disclosing if the statement of the user's intent is more similar to intent statements associated with a first response classifier (e.g., a request for an action) than to those associated with a second response classifier (e.g., a request for information), the intent confidence score for the request for an action will be higher than the request for information; see also ¶0235, ¶0238…a comparison of each vector or other representation may be performed with respect to the user input to determine a degree of correlation or similarity. In one example implementation, a cosine similarity or other similar comparison is performed between respective vectors and a score or value is determined for each pairing…a threshold score or other degree of correlation is used to select the subset of snippets)
a feedback option for receiving at least one of a positive feedback and a negative feedback, the positive feedback indicating acceptance of the recommendation and the negative feedback indicating rejection of the recommendation; and the generated recommendation on at least one user interface. (Hamid ¶0244 disclosing the generative content service may also receive express feedback provided via the interface regarding the suitability or accuracy of the results; generative content service may also provide feedback that results from object selections, dwell time on the generative response, subsequent queries, and other user interaction events that may signal positive or negative feedback, which may be used to train intent recognition modules or other aspects of the system to improve the accuracy and performance of subsequent responses; ¶0247 disclosing generative content service or a related service may receive feedback or user validation from user accounts…in response to receiving a positive feedback from an account flagged as having appropriate subject matter expertise (e.g., associated subject matter expertise has a threshold similarity to the subject matter of the generative response), the service or system may designate the generative response as verified or endorsed; see also Fig. 5A)
Claim 11: The non-transitory computer-readable medium of claim 10, the instructions being executable by the processing resource to modify subsequent recommendations, indicating linking of one or more support items with one or more first items, based on the positive feedback and the negative feedback received for the generated recommendation. (Hamid ¶0244 disclosing the generative content service may also receive express feedback provided via the interface regarding the suitability or accuracy of the results; generative content service may also provide feedback that results from object selections, dwell time on the generative response, subsequent queries, and other user interaction events that may signal positive or negative feedback, which may be used to train intent recognition modules or other aspects of the system to improve the accuracy and performance of subsequent responses; ¶0247 disclosing generative content service or a related service may receive feedback or user validation from user accounts…in response to receiving a positive feedback from an account flagged as having appropriate subject matter expertise (e.g., associated subject matter expertise has a threshold similarity to the subject matter of the generative response), the service or system may designate the generative response as verified or endorsed; see also Fig. 5A; ¶0238 disclosing a threshold score or other degree of correlation is used to select the subset of snippets; a threshold number of top scoring results are selected; the top-scoring results that provide a threshold number of characters or aggregated snippet size are selected; see also ¶0239; ¶0221 disclosing determining intent confidence scores for a natural language input with respect to each of a set of request classifiers, the intent analysis module can tailor its search and generative response operations to best satisfy the user's intent; the generative content service may use the intent confidence scores to select or limit the particular content stores that are searched in order to find content items with which to generate a response to the user; generative content service may use intent confidence scores with respect to different request classifiers to tailor its operations in various ways, including selecting a search target data store, response type, response formatting, follow-up options, response appearance, selectable options or links (e.g., what types of selectable options or links are included in a response))
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DIONE N SIMPSON whose telephone number is (571)272-5513. The examiner can normally be reached M-F; 7:30 a.m.-4:30 p.m..
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DIONE N. SIMPSON
Primary Examiner
Art Unit 3628
/DIONE N. SIMPSON/Primary Examiner, Art Unit 3628