DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 have been examined.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication 11238102 to Lisuk et al. (“Lisuk”) in view of U.S. Patent 10747962 to Fuerstenau et al. (“Fuerstenau”).
Regarding claim 1, Lisuk discloses:
1. One or more non-transitory, computer-readable storage media comprising instructions recorded thereon, wherein the instructions, when executed by at least one data processor of a computing system, cause the computing system to: See Lisuk, Fig. 7 elements 702, 722 and 728, depicting storage media, instructions, and a processor.
receive a natural language prompt relating to items in a block-based data structure of a multimodal content management system; Lisuk, Fig. 3, element 330, “Training Data.” Also see col. 2, lines 42-47, “The data analysis system may then perform a keyword comparison to identify any token (e.g., word, term, phrase, etc.) within the query that corresponds to an object in a data object model associated with underlying data (e.g., enterprise data stored in one or more databases).” Also col. 4, lines 40-45, e.g. “Data analysis system 110 can receive a user-submitted free form query (e.g., a question) pertaining to data in datastore 105. For example, this user query may be entered in a user interface provided by the data analysis system 110 and presented on one of client devices 130. The user query may be entered using natural human language …”
using the natural language prompt, generate, by a training engine of the multimodal content management system, training data comprising a set of tokens relating to a question about items in the block-based data structure; Lisuk, Fig. 3, element 320, “Training Engine.” Also see col. 2 lines 42-47 as cited above, e.g. “identify any token (e.g., word, term, phrase, etc.) within the query.”
using the training data, cause a neural network to generate a first set of response [data] … and a second set of response [data] …, Lisuk col. 8 lines 41-45, “When user interface module 210 receives a selection of an element corresponding to that objects, the corresponding portions of the dataset can be retrieved from datastore 105 and presented in the user interface.” Also col. 10, lines 29-37, “Based on the machine learning model(s) 274, the machine learning engine 310 may obtain an output 325 including one or more artifacts capable of providing a response to the natural language query, as well as an assessment of a quality of the responses (e.g., a dynamic relevance score). The data analysis system 110 may select the artifact with the highest dynamic relevance score and execute it to provide a response to the natural language query.” Also col. 10, lines 52-59, e.g. “neural networks.” Also col. 11, lines 11-13, “The output may include one or more artifacts and optionally a dynamic relevance score for each of the one or more artifacts.”
Lisuk does not expressly disclose response tokens. This is taught by Fuerstenau. See Fuerstenau, col. 9 lines 57-60, “The model 202 may learn from the sequencing of the input tokens relative to one another, and use that learning to position tokens of the output relative to one another.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Fuerstenau’s output tokens with Lisuk’s input in order to provide a useful output representation based upon a learned arbitrary input as suggested by Fuerstenau (see col. 3 lines 35-60 and col. 9 lines 52-65).
Lisuk also discloses:
wherein the first set of response tokens is based on a keyword included in the training data, the keyword relating to the at least one of a block title, a block identifier, block content or a block property, and Lisuk, col. 9, lines 19-27, “Object identifier 220 may further determine whether any of the tokens in the data string 272 represent a property of an object. For example, if the word “customer” is present in the data string “How many customers under age 30 with high spend did we have in the last two years?,” “customer” may correspond to an object. The subsequent token “under age 30” from the data string 272 may represent a property (i.e., age characteristic) of the “customer” object.” Also see col. 15 lines 21-32, e.g. “In one implementation, user interface module 210 may modify, in the user interface 600, the user input to visually indicate one or more portions 612 of the natural language query that each represent an object. … In the illustrated implementation, the portion 612 including the text “customers” was recognized as an object and presented as a selectable interface element.”
wherein the second set of response tokens is based on an inference made by the neural network about the at least one of the block title, block identifier, block content or block property; and Lisuk, col. 9 lines 45-49, “For example, machine learning subsystem 225 may provide the data string 272 and the objects derived from the data string 272 as input to the machine learning model(s) 274, and obtain information identifying one or more relevant artifacts as the output of the machine learning model(s) 274.” Also see col. 15, lines 58-60, “Response field 620 may include one or more responses, such as response 622, to the natural language query based on data from the dataset.”
generate and display, via a graphical user interface (GUI) associated with the training engine, a labeler GUI configured to display the natural language prompt, the keyword, the inference and See Lisuk, col. 15, e.g. “In one implementation, the user interface 600 includes a user input field 610 and a response field 620. A user can enter a natural language query into user input field 610. … In one implementation, user interface module 210 may modify, in the user interface 600, the user input to visually indicate one or more portions 612 of the natural language query that each represent an object. … In one implementation, user interface module 210 may further modify the user input to visually indicate one or more portions 614 of the natural language query that each represent a first class concept.” Also see Lisuk Fig. 6:
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at least one navigable link to a unit in the block-based data structure, wherein the at least one navigable link is automatically generated using a particular block title, block identifier, block content, or block property relating to the keyword or the inference. Lisuk, col. 15, lines 33-38, “The selectable interface element may include a button, link, menu, widget, or other element. In response to receiving a selection of the selectable interface element, user interface module 210 may display the data from the dataset corresponding to the object associated with the selectable interface element.”
Regarding claim 2, Lisuk also discloses:
2. The media of claim 1, wherein the at least one navigable link is operable to generate a content viewer control, configured to display the unit in the block-based data structure, while the GUI continues to display the natural language prompt, the keyword and the inference. Lisuk, col. 15, lines 33-38, “The selectable interface element may include a button, link, menu, widget, or other element. In response to receiving a selection of the selectable interface element, user interface module 210 may display the data from the dataset corresponding to the object associated with the selectable interface element.” Also see col. 16, lines 2-9, “In another implementation, however, response field 620 includes a command button 624 that, when selected, causes the artifact that generated response 622 to be maintained in the response field 620 (e.g., “pinned”). In this implementation, the artifact may be maintained in the response field 620 until a second command is received causing the artifact to be removed from the response field 620.”
Regarding claim 3, Lisuk also discloses:
3. The media of claim 1, wherein the neural network is additionally trained on two or more of: (i) block type data; (ii) block dependency data; (iii) block content values, (iv) block content type data; or (v) block format data. Lisuk, col. 2 lines 51-54, “A data object model is represented by an ontology which defines objects derived from the underlying data, properties of the objects, and relationships between the objects.”
Regarding claim 4, Lisuk also discloses:
4. The media of claim 1, wherein the generated training data comprises a temporal indication that relates to an age of the at least one of the block title, block identifier, block content or block property. See Lisuk, Fig. 6, element 612, e.g. “last five years.”
Regarding claim 5, Lisuk also discloses:
5. The media of claim 1, wherein the instructions, when executed by the at least one data processor of the computing system, cause the computing system to: responsive to receiving a user input via the GUI, automatically associate a label with at least one of the first set of response tokens or the second set of response tokens, wherein the label is determined based on the user input. See Lisuk, Fig. 6, element 626 along with col. 16, lines 9-17, “In addition, response field 620 may include a user feedback interface 626 by which the user can provide feedback evaluating the presented response. For example, the user may indicate whether or not the response 622 is appropriate (e.g., helpful) for the entered natural language query in user input field 610. This feedback can be used to further refine the machine learning model used to identify responses to additional queries in the future.”
Regarding claim 6, Lisuk also discloses:
6. The media of claim 1, wherein the instructions, when executed by the at least one data processor of the computing system, cause the computing system to: responsive to receiving a user input via the GUI, automatically generate a tuning recommendation for the neural network based at least in part on the user input. See Lisuk, Fig. 6, element 626 along with col. 16, lines 9-17, “In addition, response field 620 may include a user feedback interface 626 by which the user can provide feedback evaluating the presented response. For example, the user may indicate whether or not the response 622 is appropriate (e.g., helpful) for the entered natural language query in user input field 610. This feedback can be used to further refine the machine learning model used to identify responses to additional queries in the future.”
Regarding claim 7, Lisuk also discloses:
7. The media of claim 1, wherein at least one of the first set of response tokens or the second set of response tokens generated by the neural network is sufficient to generate a code unit executable against the block-based data structure. See Lisuk, col. 12, lines 36-39, “The one or more artifacts may include one or more pieces of logic (i.e., code) that can be executed against a dataset to identify a data portion of the dataset corresponding to the one of the one or more objects.”
Regarding claim 8, Lisuk discloses:
8. A computer-implemented method, the method comprising: See Lisuk, Fig. 4 broadly depicting a method.
All further limitations of claim 8 have been addressed in the above rejection of claim 1.
Regarding claims 9-14:
Parent claim 8 is addressed above. All further limitations of claims 9-14 have been addressed in the above rejections of claims 2-7, respectively.
Regarding claim 15, Lisuk discloses:
15. A computing system comprising at least one data processor and one or more non-transitory, computer-readable storage media comprising instructions recorded thereon, wherein the instructions, when executed by the at least one data processor, cause the computing system to: See Lisuk, Fig. 7, depicting a computing system.
All further limitations of claim 15 have been addressed in the above rejection of claim 1.
Regarding claims 16-20:
Parent claim 15 is addressed above. All further limitations of claims 16-20 have been addressed in the above rejections of claims 2-6, respectively.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Patent Application Publication 20200394260 by Wu et al. See ¶ 0009, “It can be ascertained that the deep model can output multiple answers to the query, with each answer having a score that is at or above the predefined threshold.”
U.S. Patent Application Publication 20200074984 by Ho et al. See Fig. 7 and ¶ 0061, “For example, the user may issue a query related to the intent “teaching” and the server may retrieve and return the 100 best matching utterances initially believed to be relevant to the “teaching” semantic scope of intent. The user then is presented or identifies a selected subset of the matching utterances retrieved by the server and manually labels the top N (e.g., the top 10 best matching) presented utterances (step 706).”
U.S. Patent 11966704 to Magary et al. See col. 17, lines 46-53, “In some embodiments, the model output verification software application 102 may generate the outputs 122 to include at least one of a citation to a data source of the outputs 122 (or portion(s) thereof), a network link (e.g., a URL, an IP address) to the data source of the outputs 122 (or portion(s) thereof), or a score (e.g., a reliability score, a verification score) associated with the data source of the output.”
U.S. Patent Application Publication 20220215183 by Freitag et al. See ¶ 0025, “Predicted output (such as edited text 110) can be generated, token-by-token.”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to James D Rutten whose telephone number is (571)272-3703. The examiner can normally be reached M-F 9:00-5:30 ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Li B Zhen can be reached at (571)272-3768. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/James D. Rutten/Primary Examiner, Art Unit 2121
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