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 .
Status of Claims
Claims 1-3, 7, 10-11, 15, and 18-30 are pending of which claims 1, 10 and 18 are in independent form.
Claims 1-3, 7, 10-11, 15, and 18-30 are rejected under 35 U.S.C. 101.
Claims 1-3, 7, 10-11, 15, and 18-30 are rejected under 35 U.S.C. 103.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-3, 7, 10-11, 15, and 18-30 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Regarding the 35 USC 101 (Abstract Idea), remarks made by the applicant.
Examiner specifies that, the newly added amendments do not overcome the 35 USC 101 rejection.
With respect to step 2A, Prong One:
The claims recite:
Receiving a user input regarding requested data,
Using an LLM to identify a data structure based on metadata,
Obtaining interaction information (timestamps/user interactions),
Determining a usage rate metric,
Determining whether the usage rate meets a threshold,
Based on determination: modify the configuration/schema, or modify the LLM configuration to reduce misinformation (training).
These steps amount to: collecting information, analyzing data, decision making based on rules, and taking actions based on results.
The steps fall into abstract data:
Mathematical Algorithm/Concept (usage rate metrics, comparing to threshold),
Mental Process (Evaluating usage pattern, determining whether the threshold is met, determining corrective actions).
Therefore, the claims are judicial exception.
Even though the claim references a “large language model (LLM)” and “metric deformation model”, it does not specify any particular improvement to the functioning of the computer or the LLM itself; rather, it uses these generic tools to process and govern data.
There are no steps performed that provides a technical improvement to the computing system itself (improved caching algorithm, improved database indexing, improved memory efficiency, improved cache eviction strategy; improved computing architecture). All the steps are generic, and conventional.
Thus, the claims recite an abstract idea (Mental Process/Mathematical/Data Manipulation algorithm).
With respect to step 2A, Prong Two:
The claims do not integrate the abstract idea into a practical application. The claims merely recite generic components:
One or more processors,
memories,
LLMs
Metadata identifying data structures
Timestamps (user interactions)
Usage rate comparison to threshold
Modifying LLMs or schema.
These components merely use generic/conventional computer components as tools to execute the abstract idea.
Examiner specifies that generic computer implementation (processor, memory..), use of know techniques (LLMs), metadata (not a technological improvement), do not provide a meaningful integration of the abstract idea into a practical application.
The claims do not:
Improve data structure design or storage mechanism
Improve LLM functionality or ML performance
Improve interaction data at a technical level
Improve computer performance or resource utilization
Provide a specific algorithm to reduce misinformation.
The limitations fail to improve hardware (no improvements to memory structure, CPU operation, storage optimization, etc.). There are also no improvements to computer functionality or any specific technical solution to a computer centric problems (the claims merely automate tasks humans perform conceptually: normalizing datasets and mapping relations). The claims fail to provide a particular technological solutions (such as how conversion occur). The computer merely used as a tool, which is an abstract improvement to information presentation and not technical improvements. There is no recitation of, a new data structure that changes computer operation, improved communication, an unconventional indexing/conversion technique, a specific hardware solution.
Instead the claims recite conventional and generic computer functions performed in a routine manner, which does not amount to a practical application.
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 1-3, 7, 10-11, 15, and 18-30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
The claim(s) recite(s) a system for interaction-based data governance obtaining interaction information associated with data structures.
With respect to step 1 of the patent subject matter eligibility analysis, the claims are directed to a process, machine, manufacture, or composition of matter. Independent Claim 1 is directed to a system, including one or more processors and one or more memories, which is a machine, and directed to one of the 4 categories of patent eligible subject matter. Independent Claim 18 is directed to a non-transitory computer readable medium, which is directed to one of the 4 categories of patent eligible subject matter. Independent claim 10 is directed to a method, which is a process. All other claims depend on claims 1, 10 and 18. As such, claims 1-20 are directed to a statutory category.
Regarding claims 1, 10 and 18:
With respect to step 2A, Prong One, prong one, the claims recite an abstract idea, law of nature, or natural phenomenon. Specifically, the following limitations recite mathematical concepts and/or mental processes and/or certain methods of organizing human activity.
The claims recite:
Receiving a user input regarding requested data,
Using an LLM to identify a data structure based on metadata,
Obtaining interaction information (timestamps/user interactions),
Determining a usage rate metric,
Determining whether the usage rate meets a threshold,
Based on determination: modify the configuration/schema, or modify the LLM configuration to reduce misinformation (training).
These steps amount to: collecting information, analyzing data, decision making based on rules, and taking actions based on results.
The steps fall into abstract data:
Mathematical Algorithm/Concept (usage rate metrics, comparing to threshold),
Mental Process (Evaluating usage pattern, determining whether the threshold is met, determining corrective actions).
Therefore, the claims are judicial exception.
Even though the claim references a “large language model (LLM)” and “metric deformation model”, it does not specify any particular improvement to the functioning of the computer or the LLM itself; rather, it uses these generic tools to process and govern data.
There are no steps performed that provides a technical improvement to the computing system itself (improved caching algorithm, improved database indexing, improved memory efficiency, improved cache eviction strategy; improved computing architecture). All the steps are generic, and conventional.
Thus, the claims recite an abstract idea (Mental Process/Mathematical/Data Manipulation algorithm).
With respect to step 2A, Prong Two, prong two, the claims do not recite additional elements that integrate the judicial exception into a practical application. The following limitations are considered “additional elements” and explanation will be given as to why these “additional elements” do not integrate the judicial exception into a practical application.
The claims do not integrate the abstract idea into a practical application. The claims merely recite generic components:
One or more processors,
memories,
LLMs
Metadata identifying data structures
Timestamps (user interactions)
Usage rate comparison to threshold
Modifying LLMs or schema.
These components merely use generic/conventional computer components as tools to execute the abstract idea.
Examiner specifies that generic computer implementation (processor, memory..), use of know techniques (LLMs), metadata (not a technological improvement), do not provide a meaningful integration of the abstract idea into a practical application.
The claims do not:
Improve data structure design or storage mechanism
Improve LLM functionality or ML performance
Improve interaction data at a technical level
Improve computer performance or resource utilization
Provide a specific algorithm to reduce misinformation.
The limitations fail to improve hardware (no improvements to memory structure, CPU operation, storage optimization, etc.). There are also no improvements to computer functionality or any specific technical solution to a computer centric problems (the claims merely automate tasks humans perform conceptually: normalizing datasets and mapping relations). The claims fail to provide a particular technological solutions (such as how conversion occur). The computer merely used as a tool, which is an abstract improvement to information presentation and not technical improvements. There is no recitation of, a new data structure that changes computer operation, improved communication, an unconventional indexing/conversion technique, a specific hardware solution.
Instead the claims recite conventional and generic computer functions performed in a routine manner, which does not amount to a practical application.
With respect to Step 2B. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitations are directed to a computer readable storage medium, computer, memory, and processor, at a very high level of generality and without imposing meaningful limitations on the scope of the claim. In addition, pages 2-5 of the published instant specification describe generic off‐the‐shelf computer‐based elements for implementing the claimed invention, which does not amount to significantly more than the abstract idea and is not enough to transform an abstract idea into eligible subject matter. Such generic, high‐level, and nominal involvement of a computer or computer‐based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent‐eligible, as noted at pg.74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo. Further, See, e.g., Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359‐60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093‐94 (Fed. Cir. 2015) ("Just as Diehr could not save the claims in Alice, which were directed to 'implement[ing] the abstract idea of intermediated settlement on a generic computer', it cannot save O/P's claims directed to implementing the abstract idea of price optimization on a generic computer.") (citations omitted). See also, Affinity Labs of Texas LLC v. DirecTV LLC, 838 F.3d 1253, 1257‐1258 (Fed. Cir. 2016) (mere recitation of a GUI does not make a claimpatent‐eligible); Intellectual Ventures I LLC v. Capital One Bank, 792 F.3d 1363, 1370 (Fed. Cir. 2015) ("the interactive interface limitation is a generic computer element".).
The additional elements are broadly applied to the abstract idea at a high level of generality ("similar to how the recitation of the computer in the claims in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer,") as explained in MPEP § 2106.05(f)) and they operate in a well‐understood, routine, and conventional manner.
MPEP § 2106.0S(d)(II) sets forth the following:
The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
• Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec ... ; TLI Communications LLC v. AV Auto. LLC ... ; OIP Techs., Inc., v. Amazon.com, Inc ... ; buySAFE, Inc. v. Google, Inc ... ;
• Performing repetitive calculations, Flook ... ; Bancorp Services v. Sun Life ... ;
• Electronic recordkeeping, Alice Corp ... ; Ultramercial ... ;
• Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc ... ;
• Electronically scanning or extracting data from a physical document, Content Extraction and Transmission, LLC v. Wells Fargo Bank ... ; and
• A web browser's back and forward button functionality, Internet Patent
• Corp. v. Active Network, Inc. ...
. . . Courts have held computer-implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim as a whole amounts to nothing more than generic computer functions merely used to implement an abstract idea, such as an idea that could be done by a human analog (i.e., by hand or by merely thinking).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself.
The dependent claims have been fully considered as well, however, similar to the findings for claims above, these claims are similarly directed to the “Mental Processes” grouping of abstract ideas set forth in the 2019 PEG, without integrating it into a practical application and with, at most, a general purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims. The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea.
Regarding claims 2 and 19:
The claims recite:
Generating a query, which is merely data retrieval/information generation, which considered abstract.
There is no specific database improvement, query optimization technique, or new data structure is claimed. This a simply adds another mental process/insignificant application (formulating a query), performed by generic processor.
The claims also fail Step 2A, Prong 2, because generating a query is a standard information processing function, not a technological improvement.
Regarding claims 3, 11 and 20:
The claims recite:
Providing data responsive to the query: basic retrieval.
Receiving user feedback associated with the data: collection data.
Interaction information…: classification of data.
These are all information presentation and collection operation, which are considered abstract.
There are no new algorithm, UI improvement, or computer functionality improvement recited.
The claims also fail Step 2A, Prong 2, because generating and presenting information is abstract and conventional.
Regarding claims 4 and 12: (Canceled).
Regarding claims 5 and 13: (Canceled).
Regarding claims 6 and 14: (Canceled).
Regarding claims 7 and 15:
The claims recite:
Indication of whether usage rate satisfies the threshold.
This is information output with rule-based indictor. Providing an indication is merely displaying information wit is abstract. Threshold based evaluations are abstract rules. There is no technological improvement.
The claims also fail Step 2A, Prong 2, and only adds conditional rules which is abstract.
Regarding claims 8 and 16: (Canceled).
Regarding claims 9 and 17: (Canceled).
Regarding claims 21 and 27:
The claims recite:
Interaction information including chronology of interactions.
This is merely data logging and organization. Data logging and organization is merely displaying information wit is abstract (Mental Process). Does not improve how timestamps are generated, stored, or processed. There is no technological improvement.
The claims also fail Step 2A, Prong 2, and only adds conditional rules which is abstract.
Regarding claims 22 and 28:
The claims recite:
Interaction information including user feedback indicating satisfaction.
This is merely data gathering. Data gathering is merely displaying information wit is abstract (Mental Process). Does not improve the feedback process or UI technology. There is no technological improvement.
The claims also fail Step 2A, Prong 2, and only adds conditional rules which is abstract.
Regarding claims 23 and 29:
The claims recite:
Interaction information including query generated in association with the data structure.
This is merely information collection. This is merely expanding the dataset used for analysis. Does not improve query generation, parsing or execution. There is no technological improvement.
The claims also fail Step 2A, Prong 2, and only adds conditional rules which is abstract.
Regarding claims 24 and 30:
The claims recite:
Usage rate is determined based on interaction information aggregated across multiple user requests over time.
Aggregating data across users and time is a basic statistic/data analysis technique. Calculating a usage metric is a mathematical algorithm. This merely specifies how data is combined for abstract data analysis. Does not improve aggregation algorithm or distributed processing. There is no technological improvement.
The claims also fail Step 2A, Prong 2, and only adds conditional rules which is abstract.
Regarding claim 25:
The claim recites:
Usage rate is based on the frequency with which the data structure is queried over time.
Measuring frequency is a fundamental statistical/mathematical calculation. Counting occurrence over time is considered mental process. This merely the metrics used in the abstract data analysis. Does not improve monitoring systems or query engines. There is no technological improvement.
The claims also fail Step 2A, Prong 2, and only adds conditional rules which is abstract.
Regarding claim 26:
The claim recites:
Identifying the data structure without accessing the underlaying stored data.
Identifying based on metadata rather than content is data selection/classification. This is considered a conceptual distinction, rather than a technical improvement. This is merely directed to selecting information, not improving the computer functionality. No new indexing, retrieval mechanism, or data access technique is required. There is no technological improvement.
The claims also fail Step 2A, Prong 2, and only adds conditional rules which is abstract.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-3, 7, 10-11, 15, and 18-30 are rejected under 35 U.S.C. 103 as being unpatentable over Rogynskyy; Oleg et al. (US 12216692 B1) [Rogynskyy] in view of Gupta; Suchitra et al. (US 20250077581 A1) [Gupta].
Regarding claim 1, Rogynskyy discloses, a system for interaction-based data governance, the system comprising: one or more memories; and one or more processors, coupled to the one or more memories (Fig. 24), configured to:
identify, based on obtaining user input associated with data requested by a user and using a large language mode (LLM), a data structure of a plurality of data structures, wherein the data structure is identified based on metadata associated with the plurality of data structures and independent from actual data stored in the data structure (using LLM to process data and generate output [abstract], [col. 1, ll. 41-col. 2, ll. 24], [col. 86, ll. 46-col. 87-ll. 18], [col. 115, ll. 59-col. 116, ll. 25]. Identifying and processing record objects, which are explicitly defined as data structures [col. 56, ll. 41-51], [col. 115, ll. 59-col. 116, ll. 25], [col. 119, ll. 29-43]);
obtain interaction information comprising one or more timestamps associated with one or more user interactions with the data structure (tracks user interactions with record objects, counts interactions over predetermined time frame (e.g., week, month, …) [col. 46, ll. 29-56]);
determine, based on the interaction information and using a [metric determination model] associated with the plurality of data structures, a metric usage rate (user interaction counter over a predetermined time period [col. 46, ll. 29-56]. Examiner has interpreted “usage” rate as interaction rate over time) associated with the data structure (data structures [col. 56, ll. 41-51], [col. 115, ll. 59-col. 116, ll. 25], [col. 119, ll. 29-43]);
determine whether the usage rate satisfies a usage rate threshold associated with the data structure (if the count is above/below a predetermined threshold [col. 46, ll. 29-56]. Also see [col. 29, ll. 31-col. 30, ll. 17], [col. 36, ll. 16-30], [col. 95, ll. 6-42]);
automatically modify, based on the usage rate satisfying the usage rate threshold, at least one of a configuration, associated with the data structure, or a schema, associated with the data structure, to resolve one or more errors associated with the usage rate (updating data stored in data structure over time/updating the set of text strings…stored in data structure [col. 115, ll. 59-col. 116, ll. 25]).
However, Rogynskyy does not explicitly facilitate metric determination model; automatically modify, based on the usage rate failing to satisfy the usage rate threshold, a configuration associated with the LLM to reduce misidentification of the data structure by the LLM.
Gupta discloses, metric determination model (tracking user click-trough rates, create a feedback loop, to influence the LLM ¶ [0044], [0059]. Examiner specifies that, this is rate-based metric determination and model driven evaluation);
automatically modify, based on the usage rate failing to satisfy the usage rate threshold, a configuration associated with the LLM to reduce misidentification of the data structure by the LLM (modify the one or more LLMs based on click-through rates, feedback loop to influence the LLM ¶ [0044], [0059]).
It would have been obvious to one ordinary skilled in the art at the time of the filing of the present invention to combine the teachings of the cited references because Gupta’s system would have allowed Rogynskyy to facilitates metric determination model; automatically modify, based on the usage rate failing to satisfy the usage rate threshold, a configuration associated with the LLM to reduce misidentification of the data structure by the LLM. The motivation to combine is apparent in the Rogynskyy's reference, because there is a need for an improved system and a method for dynamically recommending a set of potential courses of actions that a user may choose from, within a search query in at least one of a generative artificial intelligence (AI) environment, and a conversation AI environment, to address at least the aforementioned issues.
Regarding claims 2 and 19, the combination of Rogynskyy and Gupta discloses, generate a query associated with the data structure and based on the user input (Rogynskyy: The query engine 1920 can formulate or generate the queries based on the natural language queries of requests and/or identifications of record objects included in the requests, such as by inputting the natural language queries and/or identifications of record objects into a large language model with instructions to generate the queries. The query engine 1920 can use the queries to retrieve the data from the different sources 2006, 2008, and/or 2010. The query engine 1920 can aggregate or otherwise combine the retrieved data together, in some cases with the natural language query that initiated the retrieval, into a prompt [col. 133, ll. 5-16]. Also see [col. 9, ll. 15-19]).
Regarding claims 3, 11 and 20, the combination of Rogynskyy and Gupta discloses, provide data responsive to the query (Rogynskyy: generating an output to the query [col. 5, ll. 10-18]. Also see Fig. 14, 23); and
receive user feedback associated with the data responsive to the query, wherein the interaction information includes at least one of the query, the user feedback, or information associated with the data structure (Gupta: completion status of initiated user action through recommended the set of potential courses of actions (next best actions (NBAs)), feedback loops, feedback from users, query parameters, additional query parameters, deep integration parameters, up-sell/x-sell product links, tracked user click-through rates, any other data, and combinations thereof ¶ [0028]. Also see ¶ [0044], [0049], [0059], [0065]).
Regarding claims 4 and 12, (Canceled).
Regarding claims 5 and 13, (Canceled).
Regarding claims 6 and 14, (Canceled).
Regarding claims 7 and 15, the combination of Rogynskyy and Gupta discloses, provide an indication associated with whether the usage rate satisfies the usage rate threshold (Rogynskyy: if the count is above/below a predetermined threshold [col. 46, ll. 29-56]. Also see [col. 29, ll. 31-col. 30, ll. 17], [col. 36, ll. 16-30], [col. 95, ll. 6-42]).
Regarding claims 8 and 16, (Canceled).
Regarding claims 9 and 17, (Canceled).
Regarding claim 10, the combination of Rogynskyy and Gupta clearly show a method for performing the process for the system in claims 1 and 3. Therefore, the rejections of claims 1 and 3 applies to claim 10.
Regarding claim 18, the combination of Rogynskyy and Gupta clearly show a non-transitory computer-readable medium for performing the process for the system in claims 1, 4 and 7. Therefore, the rejections of claims 1, 4 and 7 applies to claim 18.
Regarding claims 21 and 27, the combination of Rogynskyy and Gupta discloses, wherein the interaction information comprises a chronology of interactions with the data structure (Rogynskyy: In a non-limiting example, these chunks can include text strings associated with attributes that satisfy a predefined threshold or condition (e.g., prioritizing text strings by focusing on emails where the urgency flags are set to high). The data processing system can use the generated chunks for iterative processing using the large language model. For instance, a first chunk, based on chronological order (e.g., earliest activities), can be processed first for initial insights [col. 100, ll. 60-67]), and
wherein each interaction in the chronology is associated with a respective timestamp of the one or more timestamps (Rogynskyy: tracks user interactions with record objects, counts interactions over predetermined time frame (e.g., week, month, …) [col. 46, ll. 29-56]).
Regarding claims 22 and 28, the combination of Rogynskyy and Gupta discloses, wherein the interaction information includes user feedback indicating a level of satisfaction associated with data provided from the data structure (Gupta: feedback YN may capture user feedback for the recommended set of potential courses of actions (NBAs). An action of ‘Y’ from the user may be positive i.e., user found the suggested action useful, and ‘N’ may be negative i.e., user does not find the suggested action useful ¶ [0067]).
Regarding claims 23 and 29, the combination of Rogynskyy and Gupta discloses, wherein the interaction information includes one or more queries generated in association with the data structure (Gupta: the system 102 may track user click-through rates on the generated one or more responses and one or more clickable elements corresponding to the one or more search queries. In an exemplary embodiment, the system 102 may create a feedback loop corresponding to the tracked user click-through rates to influence the one or more LLMs to reinforce and generate similar one or more responses and one or more clickable elements corresponding to similar one or more search queries. In an exemplary embodiment, the system 102 may modify the one or more LLMs to both seasonal patterns and user behavior patterns tracked through user click-through rates for the one or more search queries, based on the created feedback loop. The one or more clickable elements may include, but are not limited to, links, descriptions, source of information, directions, maps, website address, universal resource locator (URL), share, like button, dislike button, copy button, alternative links/buttons, and the like ¶ [0044], [0059]).
Regarding claims 24 and 30, the combination of Rogynskyy and Gupta discloses, wherein the usage rate associated with the data structure is determined based on interaction information aggregated across a plurality of user requests associated with the data structure over a period of time (Rogynskyy: tracks user interactions with record objects, counts interactions over predetermined time frame (e.g., week, month, …) [col. 46, ll. 29-56]).
Regarding claim 25, the combination of Rogynskyy and Gupta discloses, wherein the usage rate associated with the data structure is based on a frequency with which the data structure is queried during a period of time (Rogynskyy: counting the number of time user interacts with record object…with a predetermined time frame, wherein record objects are data structures [col. 46, ll. 29-56]. Examiner has interpreted “usage” rate as interaction rate over time. Data structures [col. 56, ll. 41-51], [col. 115, ll. 59-col. 116, ll. 25], [col. 119, ll. 29-43]).
Regarding claim 26, the combination of Rogynskyy and Gupta discloses, wherein identifying the data structure comprises identifying the data structure without accessing data stored in the data structure. (Rogynskyy: data structure (e.g., a hive table) that includes attributes or other metadata regarding the text string [col. 115, ll. 59-col. 116, ll. 25], [col. 119, ll. 29-43]. Examiner hereby specifies that data structure can be identified using metadata without fully accessing underlaying data content).
Conclusion
THIS ACTION IS MADE FINAL. 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 MOHAMMAD S ROSTAMI whose telephone number is (571)270-1980. The examiner can normally be reached Mon-Fri From 9 a.m. to 5 p.m..
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, Boris Gorney can be reached at (571)270-5626. 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.
3/19/2026
/MOHAMMAD S ROSTAMI/ Primary Examiner, Art Unit 2154