CTNF 18/418,904 CTNF 93309 DETAILED ACTION 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Notice for all US Patent Applications filed on or after March 16, 2013 07-06 AIA 15-10-15 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 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. Status of the Claims This communication is in response to communications received on 1/22/24. Claim(s) none is/are amended, claim(s) none is/are cancelled, claim(s) none is/are new, and applicant does not provide any information on where support for the amendments and/or new claims can be found in the instant specification as there are not any amendments and/or new claims. Therefore, Claims 1-20 is/are pending and have been addressed below. Response to Arguments There are no arguments. Claim Rejections - 35 USC § 112 07-30-02 AIA The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. 07-34-01 AIA Claim (s) 8 and 17 is/are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph , as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention. Claim(s) 8 and 17 is/are rejected . Claim(s) 8 and 17 state(s) the limitation “wherein the plurality of objects in the bookmark are stored in a plurality of different buckets in the cloud storage.” Thus claim(s) 8 and 17 is/are indefinite because it is unclear how objects which are exported (stored) in a bucket in a cloud storage are now stored in a different buckets and thus it is unclear if the limitation is intended to be a) wherein the plurality of objects selected are stored in a plurality of different buckets in the cloud storage and wherein the created bookmark comprises the plurality of objects selected that are stored in a plurality of different buckets in the cloud storage, b) wherein the plurality of objects selected are stored in a plurality of different buckets in the cloud storage and wherein a second bookmark is created wherein the second bookmark comprises the plurality of objects stored in a plurality of different buckets in the cloud storage, or c) something else. Appropriate correction/clarification is required. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claim(s) 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter as noted below. The limitation(s) below for representative claim(s) 1, 10, and 12 that, under its broadest reasonable interpretation, is directed to machine learning datasets in cloud storage. Step 1 : The claim(s) as drafted, is/are a process (claim(s) 1-9 recites a series of steps) and system (claim(s) 10-20 recites a series of components). Step 2A – Prong 1 : The claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) (emphasis added): Claim 1: selecting, by one or more processors , a plurality of objects for training a machine learning model; processing, by one or more processors , the plurality of objects by composing a dataset with the plurality of objects; exporting, by one or more processors , the dataset to a bucket in a cloud storage ; and creating, by one or more processors , a bookmark, wherein the bookmark comprises references to at least one of the plurality of objects . Claim(s) 10 and 19: same analysis as claim(s) 1. Dependent claims 2-9, 11-18 and 20 recite the same or similar abstract idea(s) as independent claim(s) 1, 10, and 19 with merely a further narrowing of the abstract idea(s): . The identified limitations of the independent and dependent claims above fall well-within the groupings of subject matter identified by the courts as being abstract concepts of: a method of organizing human activity (commercial or legal interactions including advertising, marketing or sales activities or behaviors, or business relations) because the invention is directed to economic and/or business relationships as they are associated with machine learning datasets in cloud storage. Step 2A – Prong 2 : This judicial exception is not integrated into a practical application because: The additional elements unencompassed by the abstract idea include platform, machine learning, database (claim(s) 1, 10, 19), processor (claim(s) 1), processor (claim(s) 10), system and components (claim 10), non-transitory and its components (claim 19). The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements as described above with respect to Step 2A Prong 2 fails to describe: Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a) Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition – see Vanda Memo Applying the judicial exception with, or by use of, a particular machine – see MPEP 2106.05(b) Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c) Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo. Thus the additional elements as described above with respect to Step 2A Prong 2 are merely (as additionally noted by instant specification [pg 20 para 2]) invoked as a tool and/or general purpose computer to apply instructions of an abstract idea in a particular technological environment, and/or mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do not integrate an abstract idea into a practical application (MPEP 2106.05(f)&(h)). Step 2B : The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Thus the additional elements as described above with respect to Step 2A Prong 2 are merely (as additionally noted by instant specification [pg 20 para 2]) invoked as a tool and/or a general purpose computer to apply instructions of an abstract idea in a particular technological environment, and/or mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do not integrate an abstract idea into a practical application and thus similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that the claims amount to significantly more than the abstract idea for the same reasons as set forth above (MPEP 2106.05(f)&(h)). Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-23-aia AIA The factual inquiries set forth in Graham v. John Deere Co. , 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-37-05 It has been held that a prior art reference must either be in the field of applicant’s endeavor or, if not, then be reasonably pertinent to the particular problem with which the applicant was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker , 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992). 07-21-aia AIA Claim (s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Goodsitt et al. (US 2021/0097343 A1) in view of Chirayath et al. (US 2023/0409528 A1) and Malla (US 2012/0216102 A1) . Regarding claim 1, 10, and 19 , Goodsitt teaches a method for managing artificial intelligence and machine learning (AI/ML) datasets in cloud storage, the method comprising: ( original vs citation ) selecting, by one or more processors, a plurality of objects for training a machine learning model; ( original vs citation ) processing, by one or more processors, the plurality of objects by composing a dataset with the plurality of objects [for the limitations above, see at least [0032] “Environment 100 can be configured to expose an interface for communication with other systems. Environment 100 can include computing resources 101, a dataset generator 103, a database 105, hyperparameter space 106, a model optimizer 107, a model storage 109, a model curator 111, and an interface 113. These components of environment 100 can be configured to communicate with each other, or with external components of environment 100, using a network 115.”; [0034] “Dataset generator 103 can include one or more computing devices configured to generate data.”; [0035] data (objects) stored in buckets “Database 105 can include one or more databases configured to store data for use by system 100. The databases can include cloud-based databases (e.g., AMAZON WEB SERVICES S3 buckets) or on-premises databases.”] ; ( original vs citation ) exporting, by one or more processors, the data set to a bucket in a cloud storage [see at least [0056] “As indicated at 730, create, by the machine learning data set management system, the new data set that includes the identified data objects from the one or more data sets. As discussed above, creation of the new data set may involve generating or initializing metadata (e.g., lineage data) for the new data set, making copies of data objects (or linking to data objects in the source data sets) and storing the new data set in data set storage.”; [0034] “Data storage service(s) 230 may implement different types of data stores for storing, accessing, and managing data on behalf of clients 250 as a network-based service that enables clients 250 to operate a data storage system in a cloud or network computing environment.”]. Goodsitt doesn’t/don’t explicitly teach however, in the field pertinent to the particular problem with which the applicant was concerned such as machine learning data access, Chirayath discloses a method for managing artificial intelligence and machine learning (AI/ML) datasets in cloud storage, the method comprising: selecting, by one or more processors, a plurality of objects for training a machine learning model; processing, by one or more processors, the plurality of objects by composing a dataset with the plurality of objects [for the limitations above, see at least [0032] “Environment 100 can be configured to expose an interface for communication with other systems. Environment 100 can include computing resources 101, a dataset generator 103, a database 105, hyperparameter space 106, a model optimizer 107, a model storage 109, a model curator 111, and an interface 113. These components of environment 100 can be configured to communicate with each other, or with external components of environment 100, using a network 115.”; [0034] “Dataset generator 103 can include one or more computing devices configured to generate data.”; [0054-0055] “FIG. 7 is a high-level flowchart illustrating various methods and techniques for creating new data sets from managed data sets, according to some embodiments. As indicated at 710, receive, via an interface of a machine learning data set management system, a request to create a new data set from data set(s) managed by the machine learning data set management system, the request specifying label(s) used to identify data objects from the data set(s) to create the new data set. … As indicated at 720, search, by the machine learning data set management system, respective labels of respective data objects included in the data set(s) that match the specified label(s) to identify the data objects from the data set(s) to create the new data set.”; [0056] “As indicated at 730, create, by the machine learning data set management system, the new data set that includes the identified data objects from the one or more data sets.”] ; ( original vs citation ) exporting, by one or more processors, the dataset to a bucket in a cloud storage ; and creating, by one or more processors , a bookmark, wherein the bookmark comprises references access to at least one of the plurality of objects [see at least [0056] “As indicated at 730, create, by the machine learning data set management system, the new data set that includes the identified data objects from the one or more data sets. As discussed above, creation of the new data set may involve generating or initializing metadata (e.g., lineage data) for the new data set, making copies of data objects (or linking to data objects in the source data sets) and storing the new data set in data set storage.”; [0034] “Data storage service(s) 230 may implement different types of data stores for storing, accessing, and managing data on behalf of clients 250 as a network-based service that enables clients 250 to operate a data storage system in a cloud or network computing environment.”]. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Goodsitt with Chirayath to include the limitation(s) above as disclosed by Chirayath. Doing so would improve Goodsitt’s (Goodsitt) generation of models with performance superior to models developed without such tuning via creation of high quality data sets which increase the availability of and performance of many different systems, services, and applications which rely upon the resulting high quality machine learning models to perform various operations or tasks [see at least Chirayath [0001, 0014-0016] ]. Furthermore, all of the claimed elements were known in the prior arts of a) Goodsitt and b) Chirayath and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention. Goodsitt in view of Chirayath doesn’t/don’t explicitly teach however, in the field pertinent to the particular problem with which the applicant was concerned such as data retrieval, Malla discloses ( original vs citation vs clarification: strikethrough of italicized original followed by plain lettering clarification in bold) creating, by one or more processors, a bookmark, wherein the bookmark comprises references to at least one of the plurality of objects linked content [see at least [0012, 0018] “Accordingly, the present invention provides a method for creating and managing a database of intelligent bookmarks, that provides users a better ability to manage information they have gathered by allowing users to access, search, share and rate this information. … An intelligent bookmark according to the present invention is a collection of information, including an address (e.g., a URL) for a document or other hyper-media enabled item bundled together with selected other information. The selected other information may be manually or automatically obtained from the document, the browser history leading up to the display of the document, user entered annotations, etc.”; [0060] “Based on the device, the user can choose what resolution in which to access bookmarks or bookmark metadata. … For example, accessing an intelligent bookmark via a mobile device such as a smart phone, the URL and a small version of the screenshot is likely all that would be desired or prudent to display. Yet accessing that same bookmark on a powerful, networked desktop PC may produce a high resolution, large format screenshot as well as a number of identifier information items.”; [0083] “Layering may also be added to intelligent bookmarks, such as present in image and video editing applications. For example, it is possible to annotate an intelligent bookmark with handwriting or highlighting on a “layer” above the bookmark itself, such that the addition of the annotation does not change the underlying bookmark. A view of the bookmark with or without one or more layers is possible. This layering allows users to collaborate on Intelligent Bookmarks as well. Being able to markup information gathered from the internet in a digital version (as opposed to printed material) allows users to interact more efficiently with research material. Such layering, highlighting, and markup allow some of the unique aspects of tablet PCs and PDAs, such as pen-based interactions with content, to be employed. Essentially, users are able to treat internet content as printed material by being able to easily markup and highlight the material. Being digital, however, allows users all the functionality of being to hide/save/undo changes and easily communicate them to others. Document versioning may also be integrated to keep track of changes in the intelligent bookmarks to reflect changes of the original website. Also, document versioning may be used to allow for multiple versioning of highlights and markups to the intelligent bookmarks.”]. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Goodsitt in view of Chirayath with Malla to include the limitation(s) above as disclosed by Malla. Doing so would improve Goodsitt in view of Chirayath’s (Goodsitt) generation of models with performance superior to models developed without such tuning via links to high quality data sets such as detailed bookmarks of where data is access from [see at least Malla [0011, 0060, 0083] ]. Furthermore, all of the claimed elements were known in the prior arts of a) Goodsitt in view of Chirayath and b) Malla and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention. Regarding claim 2 and 11 , modified Goodsitt teaches the method of claim 1, as well as the cloud storage . Modified Goodsitt doesn’t/don’t explicitly teach however Malla discloses ( original vs citation ) wherein the bookmark is created in a specific bucket of the cloud storage [see at least [0056] “An intelligent bookmark 44 is typically stored in a data base, either on-line or off-line (discussed further below)”; [0059] “Once created, an intelligent bookmark can be saved and accessed offline, FIG. 5A, or online, FIG. 5B. … In the online model shown in FIG. 5B, the user's computer 52 is connected to a remote server 56. The bookmark database 58 resides on the server 56.”]. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Goodsitt with Malla to include the limitation(s) above as disclosed by Malla. Doing so would improve modified Goodsitt’s (Goodsitt) generation of models with performance superior to models developed without such tuning via links to high quality data sets such as detailed bookmarks of where data is access from [see at least Malla [0011, 0060, 0083] ]. Furthermore, all of the claimed elements were known in the prior arts of a) modified Goodsitt and b) Malla and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention. Regarding claim 4 and 13 , modified Goodsitt teaches the method of claim 1, as well as at least one of the plurality of objects . Modified Goodsitt doesn’t/don’t explicitly teach however Malla discloses ( original vs citation vs clarification: strikethrough of italicized original followed by plain lettering clarification in bold) wherein the creating the bookmark comprises adding the references to the at least one of the plurality of objects of the data to the bookmark [see at least [0012, 0018] “Accordingly, the present invention provides a method for creating and managing a database of intelligent bookmarks, that provides users a better ability to manage information they have gathered by allowing users to access, search, share and rate this information. … An intelligent bookmark according to the present invention is a collection of information, including an address (e.g., a URL) for a document or other hyper-media enabled item bundled together with selected other information. The selected other information may be manually or automatically obtained from the document, the browser history leading up to the display of the document, user entered annotations, etc.”]. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Goodsitt with Malla to include the limitation(s) above as disclosed by Malla. Doing so would improve modified Goodsitt’s (Goodsitt) generation of models with performance superior to models developed without such tuning via links to high quality data sets such as detailed bookmarks of where data is access from [see at least Malla [0011, 0060, 0083] ]. Furthermore, all of the claimed elements were known in the prior arts of a) modified Goodsitt and b) Malla and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention. Regarding claim 5, 14, and 20 , modified Goodsitt teaches the method of claim 1, . Modified Goodsitt doesn’t/don’t explicitly teach however Malla discloses wherein the creating the bookmark further comprises: downloading a manifest file of the dataset, the manifest file identifying the plurality of objects [see at least [0015] “In some embodiments in accordance with the first aspect of the present invention, the identifier information includes the URL of the webpage, text within the webpage, non-text materials (e.g., images) and metaheaders. Portions of the non-text material may be scanned by optical character recognition to extract any text information contained therein.”] ; modifying the manifest file by adding or deleting at least one of the plurality of objects from the manifest file; and updating the manifest file of the dataset for the bookmark [for the limitations above, see at least [0083] “Layering may also be added to intelligent bookmarks, such as present in image and video editing applications. For example, it is possible to annotate an intelligent bookmark with handwriting or highlighting on a “layer” above the bookmark itself, such that the addition of the annotation does not change the underlying bookmark. A view of the bookmark with or without one or more layers is possible. This layering allows users to collaborate on Intelligent Bookmarks as well. Being able to markup information gathered from the internet in a digital version (as opposed to printed material) allows users to interact more efficiently with research material. Such layering, highlighting, and markup allow some of the unique aspects of tablet PCs and PDAs, such as pen-based interactions with content, to be employed. Essentially, users are able to treat internet content as printed material by being able to easily markup and highlight the material. Being digital, however, allows users all the functionality of being to hide/save/undo changes and easily communicate them to others. Document versioning may also be integrated to keep track of changes in the intelligent bookmarks to reflect changes of the original website. Also, document versioning may be used to allow for multiple versioning of highlights and markups to the intelligent bookmarks.”]. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Goodsitt with Malla to include the limitation(s) above as disclosed by Malla. Doing so would improve modified Goodsitt’s (Goodsitt) generation of models with performance superior to models developed without such tuning via links to high quality data sets such as detailed bookmarks of where data is access from [see at least Malla [0011, 0060, 0083] ]. Furthermore, all of the claimed elements were known in the prior arts of a) modified Goodsitt and b) Malla and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention. Regarding claim 6 and 15 , modified Goodsitt teaches the method of claim 1, as well as the bookmark . Modified Goodsitt doesn’t/don’t explicitly teach however Chirayath discloses ( original vs citation vs clarification: strikethrough of italicized original followed by plain lettering clarification in bold) further comprising: downloading, by one or more processors, the dataset based on the bookmark transfer from data storage method to storage in the machine learning platform [see at least [0042] “Another feature may be data set sharing 350. Data set sharing 350 may facilitate providing access to or copies of various data sets managed by machine learning data set management service 210. … In some embodiments, data set sharing may include making copies or transferring data sets to other storage locations as part of sharing that data set. Thus data set sharing 350 may orchestrate the various file or other data movement actions (e.g., instructing copies, file transfers, or other APIs) to make the data set be located for use by a recipient of data sharing.”] ; and training, by one or more processors, the machine learning model using the downloaded dataset [see at least [0022] “Created data sets, such as created data set 130, may be provided 106 for various different machine learning applications. For example, machine learning application(s) 140 may be training systems for machine learning models, which may be used to train, refine, test, or otherwise develop machine learning models.”; [0030] “This machine learning service 214 may implement model development to develop, configure, program, define, and/or otherwise execute training jobs on various machine learning models using data sets, such as data sets 234 in storage services 230 across one or more training nodes (which may include one or more respective processing devices for training, such as GPUs).”; [0041] “. For example, when a data set is used as part of training or developing a machine learning application in a machine learning service 214, this use may be recorded as part of data set lineage history.”; [0046] “Data set performance history 450 may be selected to provide a performance comparison between different model uses for training or testing on the data set.”]. Regarding claim 7 and 16 , modified Goodsitt teaches the method of claim 1, as well as the bookmark . Modified Goodsitt doesn’t/don’t explicitly teach however Chirayath discloses ( original vs citation vs clarification: strikethrough of italicized original followed by plain lettering clarification in bold) wherein operations utilizing the bookmark transfer from data storage method are processed through cloud storage APIs [see at least [0034] “Data storage service(s) 230 may be accessed via programmatic interfaces (e.g., APIs) or graphical user interfaces.”; [0056] “As indicated at 730, create, by the machine learning data set management system, the new data set that includes the identified data objects from the one or more data sets. As discussed above, creation of the new data set may involve generating or initializing metadata (e.g., lineage data) for the new data set, making copies of data objects (or linking to data objects in the source data sets) and storing the new data set in data set storage.”; [0034] “Data storage service(s) 230 may implement different types of data stores for storing, accessing, and managing data on behalf of clients 250 as a network-based service that enables clients 250 to operate a data storage system in a cloud or network computing environment.”]. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Goodsitt with Chirayath to include the limitation(s) above as disclosed by Chirayath. Doing so would improve modified Goodsitt’s (Goodsitt) generation of models with performance superior to models developed without such tuning via creation of high quality data sets which increase the availability of and performance of many different systems, services, and applications which rely upon the resulting high quality machine learning models to perform various operations or tasks [see at least Chirayath [0001, 0014-0016] ]. Furthermore, all of the claimed elements were known in the prior arts of a) modified Goodsitt and b) Chirayath and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention. Regarding claim 8 and 17 , modified Goodsitt teaches the method of claim 1, as well as wherein the plurality of objects are stored in the cloud storage (exporting, by the one or more processors, the dataset to a bucket in a cloud storage). Modified Goodsitt doesn’t/don’t explicitly teach however Malla discloses ( original vs citation ) wherein the plurality of objects in the bookmark are stored in a plurality of different buckets in the cloud storage [as noted by the 112 rejection the claim is unclear and is interpreted as wherein the data referenced in the bookmark are stored in the cloud storage, see at least [0012, 0018] “Accordingly, the present invention provides a method for creating and managing a database of intelligent bookmarks, that provides users a better ability to manage information they have gathered by allowing users to access, search, share and rate this information. … An intelligent bookmark according to the present invention is a collection of information, including an address (e.g., a URL) for a document or other hyper-media enabled item bundled together with selected other information. The selected other information may be manually or automatically obtained from the document, the browser history leading up to the display of the document, user entered annotations, etc.”]. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Goodsitt with Malla to include the limitation(s) above as disclosed by Malla. Doing so would improve modified Goodsitt’s (Goodsitt) generation of models with performance superior to models developed without such tuning via links to high quality data sets such as detailed bookmarks of where data is access from [see at least Malla [0011, 0060, 0083] ]. Furthermore, all of the claimed elements were known in the prior arts of a) modified Goodsitt and b) Malla and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention. Regarding claim 9 and 18 , modified Goodsitt teaches the method of claim 1, . Modified Goodsitt doesn’t/don’t explicitly teach however Malla discloses wherein the bookmark comprises metadata including a name, identifier, and version of the bookmark [see at least [0012, 0018] “Accordingly, the present invention provides a method for creating and managing a database of intelligent bookmarks, that provides users a better ability to manage information they have gathered by allowing users to access, search, share and rate this information. … An intelligent bookmark according to the present invention is a collection of information, including an address (e.g., a URL) for a document or other hyper-media enabled item bundled together with selected other information. The selected other information may be manually or automatically obtained from the document, the browser history leading up to the display of the document, user entered annotations, etc.”; [0052-0055] “For example, text and html information that can be extracted from the webpage are identified and saved at 14. Keywords 16 may be identified and extracted from text 14 or other portions of the webpage. … The URL of the website 18 and the parent site 20 are extracted. Other metadata information 22, present in the webpage may also be extracted based on page metaheaders 24. For example, date and time stamp information 26 can be extracted from the metaheader 24. Images and non-textual information 28 may be identified and extracted. Based on the available information, a category or categories 30 may automatically be assigned. … Based on user-selectable preferences, the user can also choose to have images and non-textual information 28 scanned by OCR (Optical Character Recognition) to extract further text and information. Further integration can be incorporated in the intelligent bookmark to generate PDF files from the website text 14 to attach to the intelligent bookmark, if necessary. … The bookmarked site can be checked for a community rating at 36 as well, allowing for the user to further update the community rating. Other features may include capturing animation/video or other display data that dynamically changes on the screen. There are a variety of COTS products that capture screen animation/video by recording the screen. … Collectively, the details captured above are referred to as identifier information 40, and the address 42 and associated identifier information 40 are collectively referred to as an intelligent bookmark 44, as shown in FIG. 2 embodied as a database record.”; [0060] “Based on the device, the user can choose what resolution in which to access bookmarks or bookmark metadata. … For example, accessing an intelligent bookmark via a mobile device such as a smart phone, the URL and a small version of the screenshot is likely all that would be desired or prudent to display. Yet accessing that same bookmark on a powerful, networked desktop PC may produce a high resolution, large format screenshot as well as a number of identifier information items.”; [0083] “Layering may also be added to intelligent bookmarks, such as present in image and video editing applications. For example, it is possible to annotate an intelligent bookmark with handwriting or highlighting on a “layer” above the bookmark itself, such that the addition of the annotation does not change the underlying bookmark. A view of the bookmark with or without one or more layers is possible. This layering allows users to collaborate on Intelligent Bookmarks as well. Being able to markup information gathered from the internet in a digital version (as opposed to printed material) allows users to interact more efficiently with research material. Such layering, highlighting, and markup allow some of the unique aspects of tablet PCs and PDAs, such as pen-based interactions with content, to be employed. Essentially, users are able to treat internet content as printed material by being able to easily markup and highlight the material. Being digital, however, allows users all the functionality of being to hide/save/undo changes and easily communicate them to others. Document versioning may also be integrated to keep track of changes in the intelligent bookmarks to reflect changes of the original website. Also, document versioning may be used to allow for multiple versioning of highlights and markups to the intelligent bookmarks.”]. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Goodsitt with Malla to include the limitation(s) above as disclosed by Malla. Doing so would improve modified Goodsitt’s (Goodsitt) generation of models with performance superior to models developed without such tuning via links to high quality data sets such as detailed bookmarks of where data is access from [see at least Malla [0011, 0060, 0083] ]. Furthermore, all of the claimed elements were known in the prior arts of a) modified Goodsitt and b) Malla and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention. Conclusion When responding to the office action, any new claims and/or limitations should be accompanied by a reference as to where the new claims and/or limitations are supported in the original disclosure. Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES WEBB whose telephone number is (313)446-6615. The examiner can normally be reached on M-F 10-3. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jerry O’Connor can be reached on (571) 272-6787. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /JAMES WEBB/Examiner, Art Unit 3624 Application/Control Number: 18/418,904 Page 2 Art Unit: 3624 Application/Control Number: 18/418,904 Page 3 Art Unit: 3624 Application/Control Number: 18/418,904 Page 5 Art Unit: 3624 Application/Control Number: 18/418,904 Page 6 Art Unit: 3624 Application/Control Number: 18/418,904 Page 7 Art Unit: 3624 Application/Control Number: 18/418,904 Page 9 Art Unit: 3624 Application/Control Number: 18/418,904 Page 10 Art Unit: 3624 Application/Control Number: 18/418,904 Page 11 Art Unit: 3624 Application/Control Number: 18/418,904 Page 12 Art Unit: 3624 Application/Control Number: 18/418,904 Page 13 Art Unit: 3624 Application/Control Number: 18/418,904 Page 14 Art Unit: 3624 Application/Control Number: 18/418,904 Page 15 Art Unit: 3624 Application/Control Number: 18/418,904 Page 16 Art Unit: 3624 Application/Control Number: 18/418,904 Page 17 Art Unit: 3624 Application/Control Number: 18/418,904 Page 18 Art Unit: 3624 Application/Control Number: 18/418,904 Page 19 Art Unit: 3624 Application/Control Number: 18/418,904 Page 20 Art Unit: 3624 Application/Control Number: 18/418,904 Page 21 Art Unit: 3624 Application/Control Number: 18/418,904 Page 22 Art Unit: 3624 Application/Control Number: 18/418,904 Page 23 Art Unit: 3624 Application/Control Number: 18/418,904 Page 24 Art Unit: 3624 Application/Control Number: 18/418,904 Page 25 Art Unit: 3624 Application/Control Number: 18/418,904 Page 26 Art Unit: 3624
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