DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Drawings
2. The drawings are objected to because detailed features of figures 5-14 (e.g., text and graphical elements) in screenshots are unclear and too blurry to be readable. See MPEP 608.02. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121 (d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 102
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.
3. Claims 1-5, 7, 11-15, 17, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ash et al. (US 2022/0292423 A1).
As in Claim 1, Ash teaches a method comprising:
receiving objective user inputs applied to a set of objective objects of a set of objects (at least pars. 223, 227, 233, 244, 256, 267-268, 533, the system receives various user inputs including objective user inputs applied to objective objects);
receiving topic user inputs applied to a set of topic objects of the set of objects (at least pars. 223, 227, 233, 244, 256, 267-268, 533, the system receives topic user inputs applied to topic objects);
receiving settlement user inputs applied to a set of settlement objects of the set of objects (at least pars. 225, 238, 529, 537, 533-534, 548, 550, the system receives settlement user inputs (e.g., comments, or actions or decisions related inputs) applied to settlement objects);
applying a guidance categorization model to a guidance object of the set of topic objects to a generate a guidance label for the guidance object (pars. 241, 273, 279-280, 544, the system, using machine learning models, can classify and label objects or content that can directly map to “guidance” or “blocker” concepts. For example, the metadata can be used to identify words or phrases (e.g., e.g., send automated emails, trigger workflows, or coaching tools), which align with “guidance” signals such as recommendations, suggestions, and next-step assistance; further see pars. 241, 247, 251-252, 279-280);
applying a blocker categorization model to a blocker object of the set of topic objects to generate a blocker label for the blocker object (pars. 241, 273, 279-280, 534-535, 534-535, 544, Similarly, the metadata can be used to identify negative or problematic states (e.g., angry or frustrated sentiment, escalation conditions, or troubleshooting-related topics described in pars. 273, 280), which align with “blocker” signals such as issues, problems, or customer difficulty; further see pars. 241, 247, 251-252, 279-280);
applying a diagram metrics model to the set of objects to generate diagram metrics data (at least pars. 88-89, 106, 93-97, 109, 115, the system generate metrics from those models. For example, the system can generate relevancy metric described at least pars. 89, 93-97, 109; further see pars. 117-118, 430-433, 445-446, 455-456); and
presenting a topic diagram using the guidance label, the blocker label, and the diagram metrics data (pars. 117-118, 430-433, 445-446, 455-456, the system presenting these metrics and classifications through visual interfaces such as charts/graphs/diagrams. For example, Topic structure or topic metrics can be presented visually (cluster/diagram-style) described in pars. 88, 109, and 115. Also topic-related or object-related analytics can be converted into visual reports (charts/graphs) described in pars. 430-431, 445-446; further see pars. 88-89, 106, 93-97, 109, 115 ).
As in Claim 2, Ash teaches all the limitations of Claim 1. Ash further teaches extracting a set of text from a set of user inputs comprising one or more of the objective user inputs, the topic user inputs, and the settlement user inputs (at least pars. 86-89, 92, 105-110, 406-415, the system can extract a set of text from user inputs comprising the objective user inputs, topic ictus, and the settlement user inputs; further see pars.142-146, 184, 187 and see rejection of claim 1); and
applying an embedding model to the set of text to generate a set of vectors for the set of objects (at least pars. 86-89, 92, 105-110, 406-415, 497-500, 501-512, the system can vectorize the extracted text (e.g., inputs, conversation, etc.)).
As in Claim 3, Ash teaches all the limitations of Claim 1. Ash further teaches
training the guidance categorization model to generate the guidance label from a vector representing the guidance object by applying the guidance categorization model to a set of training inputs to generate a set of training outputs used to generate model updates that are applied to the guidance categorization model (at least pars. 242, 247, 273, 278-283, 286, the system trains machine learning models by converting each object into a feature or entity-specific vector representation, which is then used as input to learn labels for downstream classification tasks. These learned labels include 1) negative or problematic states, such as angry, frustrated, or unresolved conditions, etc.,, and 2) recommendation, coaching, suggestion, or help-oriented actions, such as sending help from object features are paired with known labels (e.g., sentiment annotations, survey vectors. In particular, sentiment and NLP models are trained using labeled conversation transcripts to map feature vectors to emotional or tone-based classification, while feedback system continuously supply new labeled data (e.g., survey responses and interaction outcomes) to retain and refine these models in production; further see rejection of claim 1).
As in Claim 4, Ash teaches all the limitations of Claim 1. Ash further teaches
training the blocker categorization model to generate the blocker label from a vector representing the blocker object by applying the blocker categorization model to a set of training inputs to generate a set of training outputs used to generate model updates that are applied to the blocker categorization model (at least pars. 242, 247, 273, 278-283, 286, see rejection of claim 3; further see rejection of claim 1).
As in Claim 5, Ash teaches all the limitations of Claim 1. Ash further teaches
applying an execution index model to the set of objects to generate execution index data (see at least pars. 518, 540-543, index data can be generated with models; further see pars. 246, 336, 421, 461, 481); and
presenting execution index data in response to an execution user input (see at least pars. 518, 540-543, the index data can be presented to the user with user input).
As in Claim 7, Ash teaches all the limitations of Claim 1. Ash further teaches presenting an objective map with a first objective card representing a first
objective object of the set of objective objects and linked to a first user object (see FIGS. 49D-49E and at least pars. 337-341, 347-364, 376-378, 385-386);
presenting the objective map with a second objective card representing a second objective object of the set of objective objects and linked to a second user object (see FIGS. 49D-49E and at least pars. 337-341, 347-364, 376-378, 385-386); and
presenting a connection between the first objective card and the second objective card, wherein the connection represents an objective hierarchy between the first objective object and the second objective object and represents a user hierarchy between the first user object and the second user object (see FIGS. 49D-49E and at least pars. 337-341, 347-364, 376-378, 385-386).
Claims 11 and 20 are substantially similar to Claim 1 and rejected under the same rationale.
Claim 12 is substantially similar to Claim 2 and rejected under the same rationale.
Claim 13 is substantially similar to Claim 3 and rejected under the same rationale.
Claim 14 is substantially similar to Claim 4 and rejected under the same rationale.
Claim 15 is substantially similar to Claim 5 and rejected under the same rationale.
Claim 17 is substantially similar to Claim 7 and rejected under the same rationale.
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.
4. Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Ash et al. (US 2022/0292423 A1) in view of Pendyala, Mahesh (US 2022/0215017 A1).
As in Claim 6, Ash teaches all the limitations of Claim 1. Ash does not teach applying an objective mapping model to the set of objects to generate an objective map; and presenting an objective map with the set of objective objects in response to a mapping user input.
However, in the same filed of the invention, Pendyala teaches applying an objective mapping model to the set of objects to generate an objective map (FIG. 3, at least pars. 39, 41-43, 46, the system uses computational and machine learning models to process a user’s search query 302 and generate a structured hierarchical map); and
presenting an objective map with the set of objective objects in response to a mapping user input ( FIG. 3, at least pars. 39, 41-43, 46, with the user input, the system presents the structured hierarchical map as search results).
Therefore, before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify the system and method for classifying guidance and blocker objects using machine-learning models, as taught by Ash, and to provide the structured hierarchical map with the user input, as taught by Pendyala. The motivation is to visually provide a structured hierarchical map with search queries, allowing users to quickly understand and navigate the results.
Claim 16 is substantially similar to Claim 6 and rejected under the same rationale.
5. Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Ash et al. (US 2022/0292423 A1) in view of Gu et al. (US 2015/0286387 A1).
As in Claim 8, Ash teaches all the limitations of Claim 1. Ash further teaches presenting an objective map with a first objective card (see FIGS. 49D-49E).
Ash does not appear to explicitly teach that a first objective card comprises an activity icon coded to identify an activity level.
However, in the same filed of the invention, Gu teaches that a first objective card comprises an activity icon coded to identify an activity level (FIG. 24, pars. 115-116, 124, indicators such as icons, highlights, color coding, and textures can be used to represent activity levels).
Therefore, before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify the system and method for classifying guidance and blocker objects using machine-learning models, as taught by Ash, and to provide the indicators representing activity levels, as taught by Gu. The motivation is to provide visual indicators to easily and quickly analyze or understand activity levels.
Claim 18 is substantially similar to Claim 8 and rejected under the same rationale.
6. Claims 9-10 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Ash et al. (US 2022/0292423 A1) in view of Hymel, James Allen (US 20130103769 A1).
As in Claim 9, Ash teaches all the limitations of Claim 1. Ash does not teach presenting an objective table with information from the set of objective objects and the set of topic objects.
However, in the same filed of the invention, Hymel teaches presenting an objective table with information from the set of objective objects and the set of topic objects (see FIG 3, 6, at least pars. 31-33, 39).
Therefore, before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify the system and method for classifying guidance and blocker objects using machine-learning models, as taught by Ash, and to provide the object table for the email messages, as taught by Hymel. The motivation is to provide an objective table for email messages, allowing users to easily search, compare, and process data or information.
As in Claim 10, Ash teaches all the limitations of Claim 1. Ash does not teach presenting a topic table with information from the set of topic objects and the set of settlement objects.
However, in the same filed of the invention, Hymel teaches presenting a topic table with information from the set of topic objects and the set of settlement objects (see FIG 3, 6, at least pars. 31-33, 39).
Therefore, before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify the system and method for classifying guidance and blocker objects using machine-learning models, as taught by Ash, and to provide the subject table for the email messages, as taught by Hymel. The motivation is to provide a subject table for email messages, allowing users to easily search, compare, and process data or information.
Claim 19 is substantially similar to Claim 9 and rejected under the same rationale.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Rinna Yi whose telephone number is (571) 270-7752 and fax number is (571) 270-8752. The examiner can normally be reached on M-F 8:30am-5:00pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Fred Ehichioya can be reached on (571) 272-4034.
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/RINNA YI/
Primary Examiner, Art Unit 2179
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