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 are pending in this Office Correspondence (OC).
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 – 20 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.
Step 1: The claims 8 recites a “method for receiving a plurality of temporal entries; analyzing the plurality of temporal entries to generate a plurality of metadata. . . ; generating a plurality of data sets. . . ; storing the plurality of data sets. . . ; receiving a query comprising a set of query text; generating a large language model (LLM) query . .; and generating a response to the query . . .” the claim(s) recites a series of steps and, therefore, is a process
Step 2A Prong One:
"analyzing the plurality of temporal entries to generate a plurality of metadata " as drafted recites a mentally performable process as an evaluation or judgement. Please see Instant paragraphs [0031] where one can mentally evaluate to analyzing the plurality of temporal entries.
“generating a plurality of data sets” as drafted recites a mentally performable process as an evaluation or judgement. Please see Instant paragraph [0074] where one can mentally evaluate generating a plurality of data sets.
“generating a large language model (LLM) query” as drafted recites a mentally performable process as an evaluation or judgement. Please see Instant paragraphs [0077] where one can mentally evaluate to generating an LLM query where the LLM query can include at least a portion of a subset of the data sets based on the set of query text.
“generating a response to the query” as drafted recites a mentally performable process as an evaluation or judgement. Please see Instant paragraphs [0078] where one can mentally evaluate to generating a response to the query request based on an output.
These imitations are processes that, under their broadest reasonable interpretation, cover performance of the limitation in the mind, but for the recitation of generic computer components. That is, other than reciting a "database" or "processor", nothing in the claim element precludes the step from practically being performed in a human mind or with the aid of pen and paper. For example, “analyzing” and “generating” in the context of this claim encompasses a user mentally, and with the aid of pen and paper, within the plurality of command sets, analyzing the plurality of temporal entries to generate a plurality of metadata, generating a plurality of data sets, generating a large language model (LLM) query, and generating a response to the query.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two: The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements "receiving a plurality of temporal entries", “storing the plurality of data sets” and "receiving a query.” These limitations amount to a data gathering step and a mere generic transmission and presentation of collected and analyzed data which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)). The limitations represents an extra-solution activity because it is a mere nominal or tangential addition to the claim, a mere generic transmission and presentation of collected and analyzed data. (See MPEP 2106.05(g)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: The limitations "receiving” and “storing” are recognized by the courts as well-understood, routine , and conventional activities when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (see MPEP 2106.05(d)(II)(iv) Storing and retrieving information in memory, Versata Dev. Group Inc....; Receiving or transmitting data over a network, e.g., using the Internet to gather data, buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); (v) Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93). Therefore, the claim is not patent eligible.
Accordingly, claims 1 and 15 are rejected for the same rational under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Therefore, claims 1, 8 and 15 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Further the limitations in the dependent claims 2 – 7, 10 – 14 and 16 - 20, respectively, merely specify the type of the data gathered and analyzed without adding significantly more. Analysis of the dependent claims is shown below.
Claim 2 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 2 recites the same abstract idea of claim 1. The claim recites the additional limitation of “receive a plurality of listing entries, wherein the plurality of temporal entries are each associated with a respective one of the plurality of listing entries; generate a plurality of listing data sets individually comprising a respective one of the plurality of listing entries and a corresponding one of a plurality of listing metadata; and store the plurality of listing data sets in the data store in vector format”, which is equivalent to merely saying “apply it”, and amounts to no more than mere instructions to implement the abstract idea on a computer. Mere instructions to apply an exception using a generic computer does not amount to significantly more.
Claim 3 is dependent on claim 2 and includes all the limitations of claim 2. Therefore, claim 3 recites the same abstract idea of claim 2. The claim recites the additional limitation of “the LLM query comprises at least a portion of a subset of the plurality of listing data sets based on the set of query text”, which further elaborates on the abstract idea, since analyzing of information is a mental process, and therefore, does not meaningfully limits the claim.
Claim 4 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 4 recites the same abstract idea of claim 1. The claim recites the additional limitation of “the LLM query comprises a retrieval-augmented generation (RAG) query”, which further elaborates on the abstract idea, since analyzing of information is a mental process, and therefore, does not meaningfully limits the claim.
Claim 5 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 5 recites the same abstract idea of claim 1. The claim recites the additional limitation of “individual ones of the plurality of metadata comprise relative and absolute timing data for the LLM system to resolve relative timing for the LLM system”, which further elaborates on the abstract idea, since analyzing of information is a mental process, and therefore, does not meaningfully limits the claim.
Claim 6 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 6 recites the same abstract idea of claim 1. The claim recites the additional limitation of “the portion of the subset of the plurality of data sets comprises metadata from the plurality of metadata”, which further elaborates on the abstract idea, since analyzing of information is a mental process, and therefore, does not meaningfully limits the claim.
Claim 7 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 7 recites the same abstract idea of claim 1. The claim recites the additional limitation of “generate a vector representation of the set of query text; and identify the subset of the plurality of data sets based on the vector representation of the set of query text”, which further elaborates on the abstract idea, since analyzing of information is a mental process, and therefore, does not meaningfully limits the claim.
Claim 10 is dependent on claim 8 and includes all the limitations of claim 8. Therefore, claim 10 recites the same abstract idea of claim 8. The claim recites the additional limitation of “formatting the plurality of temporal entries into a structure compatible with the data store”, which further elaborates on the abstract idea, since analyzing of information is a mental process, and therefore, does not meaningfully limits the claim.
Claim 11 is dependent on claim 8 and includes all the limitations of claim 8. Therefore, claim 11 recites the same abstract idea of claim 8. The claim recites the additional limitation of “individual ones of the plurality of metadata comprise timing data for the LLM system to resolve at least one relative date”, which further elaborates on the abstract idea, since analyzing of information is a mental process, and therefore, does not meaningfully limits the claim.
Claim 16 is dependent on claim 15 and includes all the limitations of claim 15. Therefore, claim 16 recites the same abstract idea of claim 15. The claim recites the additional limitation of “parse the set of query text to identify a textual representation of a relative date; and mapping the textual representation of the relative date to an absolute date”, which further elaborates on the abstract idea, since analyzing of information is a mental process, and therefore, does not meaningfully limits the claim.
Claim 17 is dependent on claim 15 and includes all the limitations of claim 15. Therefore, claim 17 recites the same abstract idea of claim 15. The claim recites the additional limitation of “the at least one computing device to update at least one relative date in the plurality of data sets in response to determining the at least one relative date is stale”, which further elaborates on the abstract idea, since analyzing of information is a mental process, and therefore, does not meaningfully limits the claim.
Claim 18 is dependent on claim 15 and includes all the limitations of claim 15. Therefore, claim 18 recites the same abstract idea of claim 15. The claim recites the additional limitation of “individual ones of the plurality of metadata comprise timing data for the LLM system to resolve relative timing based on at least one time zone”, which further elaborates on the abstract idea, since analyzing of information is a mental process, and therefore, does not meaningfully limits the claim.
Claim 19 is dependent on claim 15 and includes all the limitations of claim 15. Therefore, claim 19 recites the same abstract idea of claim 15. The claim recites the additional limitation of “the at least one computing device to analyze the set of query text using a natural language processing algorithm to generate at least one context, wherein the LLM query is generated based on the at least one context”, which further elaborates on the abstract idea, since analyzing of information is a mental process, and therefore, does not meaningfully limits the claim.
Claim 20 is dependent on claim 15 and includes all the limitations of claim 15. Therefore, claim 20 recites the same abstract idea of claim 15. The claim recites the additional limitation of “the at least one computing device to modify the response to the query to append at least one user interface widget”, which further elaborates on the abstract idea, since analyzing of information is a mental process, and therefore, does not meaningfully limits the claim.
Therefore, claims 1 – 7, 8, 10, 11 and 15 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more than the abstract idea.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 4 – 7, 8, 10, 11 and 15 – 20 are rejected under 35 U.S.C. 103 as being unpatentable over USPAT 9,244,968 (USPGPUB 2009/0049017) issued to John Gross (“Gross”) and further in view of USPGPUB 2024/0144192 issued to Felix Weissenberger et al. (“Weissenberger”).
With respect to claims 1, 8 and 15, Gross teaches system, method and program product, comprising: a memory; and at least one computing device in communication with the memory, wherein the at least one computing device being configured to:
receive a plurality of temporal entries (Figure 1, system 100 receives documents that have temporal data, including data sets 120, 130);
analyze the plurality of temporal entries to generate a plurality of metadata individually corresponding to a respective one of the plurality of temporal entries (training classifier 110 creates temporal reference data sets 140 (and 150, 160));
generate a plurality of data sets individually comprising a respective one of the plurality of temporal entries and a corresponding one of the plurality of metadata;
store the plurality of data sets in a data store in vector format (Figure 1, see 140, 150, 160; col. 8 table 1 receive query: FIG. 4B, 9, a user can make a query to a search engine about an event, game, etc.);
generate a query comprising at least a portion of a subset of the plurality of data sets based on the set of query text (FIG. 1 the system uses the sorted and categorized data sets SDI, SD2 created by the machine classifier 110 to determine what subject the user is interested in, here, for example, the most current score of a football game generate a response: see module 180, system informs user (e.g. through a website news aggregator) what the most recent score is of the game cl. 2 listing entries - SD1, SD2 160 cl. 5 sorted entries - FIG. 3 cl. 11 timing data – Fig. 3 semantic time periods; Table 1, Table 2 col. 14); and
generate a response to the query (column 11, FIG. 1 at step 180 the ranked set can be presented as desired to persons viewing the aggregated news content at a conventional web page or web site as shown in FIG. 6).
Gross does not explicitly teach receive a query comprising a set of query text; generate a large language model (LLM) query comprising at least a portion of a subset of the plurality of data sets based on the set of query text; and generate a response to the query based on an output of an LLM system in response to the LLM query.
Weissenberger teaches receive a query comprising a set of query text (abstract: receiving a query determined to be relevant to the electronic calendar, prime a large language model (LLM) using a priming input (e.g., process the priming input using the LLM));
Weissenberger teaches generate a large language model (LLM) query comprising at least a portion of a subset of the plurality of data sets based on the set of query text (Para 0003]: using a large language model (LLM), to respond to a query related to electronic calendar(s) of user(s), after first priming the LLM using calendar data (structured or unstructured) associated with at least one of the user(s). For example, the response to the query can be generated based on output from the LLM, after the LLM is primed using the calendar data and then the query is processed using the primed LLM. Some of those implementations generate a natural language representation of the calendar data and prime the LLM based on the natural language representation); and
Weissenberger teaches generate a response to the query based on an output of an LLM system in response to the LLM query (Para [0004]: a response to the query can be generated based on LLM output that is generated after processing of the query. the response can be rendered to the user who created the message, without requiring any input from a receiving user to whom the query is directed, thereby obviating any use by the receiving user of their client device to formulate a response—and thereby conserving resources of their client device).
Both of Gross and Weissenberger are same field of endeavor and they are both in the data processing art and therefore, they are combinable/modifiable.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention was made to modify the teachings of Gross' temporal document verifier with the teachings of Weissenberger's using Large Language Model in Reducing Extent of Calender Related Interaction, in order to improve computing efficiencies and save computing resources depending on the computing circumstance when the orders of the different blocks can be altered or rearranged.
Modification would automatically verifying temporal characteristics of electronic document in news aggregators, search engines and other automated systems.
As to claim 4, the LLM query comprises a retrieval-augmented generation (RAG) query (Weissenberger, Para [0008]: Various implementations disclosed herein relate to generating a natural language representation, for structured calendar data of an electronic calendar, in advance of using the natural language representation in priming an LLM and then processing a received query using the primed LLM. Generating the natural language representation in advance can mitigate latency in processing the received query using the primed LLM, as the natural language representation is already generated and can be used immediately in priming the LLM in advance of processing the query).
As to claim 5, individual ones of the plurality of metadata comprise relative and absolute timing data for the LLM system to resolve relative timing for the LLM system (Gross, column 16, This disparity in characterization of the state of the event can be exploited, of course, so that if one document only contains a single tag with an object specifying a particular state, and a different document has several which identify the identical state, then the latter may be considered more reliable of course with respect to the temporal state of that particular event at least. So the frequency and absolute numbers of object/state tags can also be used of course to determine temporal order).
As to claim 6, the portion of the subset of the plurality of data sets comprises metadata from the plurality of metadata (Weissenberger, Para [0012]: a user may configure system settings of an electronic calendar to indicates a preference over certain time session(s) for certain activities/events (e.g., CFO prefers meetings at Tuesday 3 pm), and the preference of the user here can be stored in the preference data (referred to as “unstructured calendar data”), as part of metadata for the electronic calendar. It's noted that the preference data does not necessarily need to be data stored in association with an electronic calendar. For example, the preference data can be part of metadata associated with applications (e.g., virtual meeting) other than a calendar application (that provides one or more electronic calendars)).
As to claim 7, generate a vector representation of the set of query text; and identify the subset of the plurality of data sets based on the vector representation of the set of query text (Gross, Column 7, line 66 – column 8, line 30: At step 140 information about the individual collections (D1, D2, etc.) can be stored in a table such as the following: TABLE-US-00001 Category N Term Temporal Documents Vector Keywords Interpretation Pa1, Pa2, Pa3 . . . Pan Vfirst Wa1, Wa2, T.sub.first Wa3 . . . Pb1, Pb2 . . . V1 Wb1, Wb2 . . . T.sub.1 . . . . . . . . . Plast1, Plast2 . . . Vlast Wlast1, Wlast2 . . . T.sub.last
In other words, the natural language engine can process the documents which are correlated to a particular temporal interpretation to extract keywords which best represent or signify the presence of a document within such temporal order. For example in the context of a sports category for hockey, the keywords for different temporal interpretations may include the terms discussed above, such as {(team name), preview, upcoming, face-off, start-time, expected team lineups, injury scratches, rink conditions, shots on goal, penalties, losing, lost, winning, won, secured a victory, first period, second period, third period, final score . . . } Linguistically speaking the pairings may consist of subject/predicate pairs. A number of semantic variants will of course be included as well for such words/phrases. A combined term vector representing each respective row of interpreted documents may also be compiled for later reference. Again while shown in the context of a sports document analyzer, the same principles could be extended and used with any type of content to be analyzed for temporal qualities).
As to claim 10, formatting the plurality of temporal entries into a structure compatible with the data store (Weissenberger: Para [0042]: the first and second entries, while being displayed remotely from each (e.g., on separately calendar months or years), can be stored structurally using a standard format (e.g., table) or a data model).
As to claim 11, individual ones of the plurality of metadata comprise timing data for the LLM system to resolve at least one relative date (Weissenberger: Para [0012]: a user may configure system settings of an electronic calendar to indicates a preference over certain time session(s) for certain activities/events (e.g., CFO prefers meetings at Tuesday 3 pm), and the preference of the user here can be stored in the preference data (referred to as “unstructured calendar data”), as part of metadata for the electronic calendar).
As to claim 16, parse the set of query text to identify a textual representation of a relative date; and mapping the textual representation of the relative date to an absolute date (Gross, column 13, a conventional text/word parser could be used to shred the documents into individual words, phrases and sentences. It will be apparent that other content structures in the document, including images, graphics, audio data, and hyperlinks could be considered as well. Such items, along with any metadata associated therewith, can also be examined to determine their relative age and timeliness).
As to claim 17, update at least one relative date in the plurality of data sets in response to determining the at least one relative date is stale (Gross, column 1, automatically updates the topics and news stories on a periodic basis).
As to claim 18. individual ones of the plurality of metadata comprise timing data for the LLM system to resolve relative timing based on at least one time zone (Weissenberger: Para [0012]: a user may configure system settings of an electronic calendar to indicates a preference over certain time session(s) for certain activities/events (e.g., CFO prefers meetings at Tuesday 3 pm), and the preference of the user here can be stored in the preference data (referred to as “unstructured calendar data”), as part of metadata for the electronic calendar).
As to claim 19, the at least one computing device to analyze the set of query text using a natural language processing algorithm to generate at least one context, wherein the LLM query is generated based on the at least one context (Gross, Fig. 1, the documents may be analyzed and grouped automatically in distinct time periods (covering certain decades, certain cultural era (hippies, wars)) and presented to the user in summary groupings. In the open ended query example asking about Lincoln's life, the user may be presented with the specific categories for Lincoln as noted above. The user could then drill down and study the individual temporal categories as desired)
As to claim 20, modify the response to the query to append at least one user interface widget (Gross, column 1, automatically updates the topics and news stories on a periodic basis).
Allowable Subject Matter
Claims 2 – 3, 9 and 12 – 14 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The applied prior art of record does not explicitly teach or fairly suggest “wherein the at least one computing device is further configured to: receive a plurality of listing entries, wherein the plurality of temporal entries are each associated with a respective one of the plurality of listing entries; generate a plurality of listing data sets individually comprising a respective one of the plurality of listing entries and a corresponding one of a plurality of listing metadata; and store the plurality of listing data sets in the data store in vector format” as recited in claim 2.
The dependent claim 3, being definite, further limiting, and fully enabled by the specification could also be allowable.
The applied prior art of record does not explicitly teach or fairly suggest “transmitting, via one of the one or more computing devices, a message comprising at least one sample query to a mobile computing device; receiving, via one of the one or more computing devices, the query comprising the set of query text from the mobile computing device; transmitting, via one of the one or more computing devices, the response to the query to the mobile computing device; and receiving, via one of the one or more computing devices, a second query comprising a set of second query text from the mobile computing device” as recited in claim 9.
The applied prior art of record does not explicitly teach or fairly suggest “receiving, via one of the one or more computing devices, a plurality of listing entries; analyzing, via one of the one or more computing devices, the plurality of listing entries to generate a plurality of listing metadata individually corresponding to a respective one of the plurality of listing entries; and generating, via one of the one or more computing devices, a plurality of listing data sets individually comprising a respective one of the plurality of listing entries and a corresponding one of the plurality of listing metadata” as recited in claim 12.
The dependent claims 13 and 14, being definite, further limiting, and fully enabled by the specification could also be allowable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Veillon (USPAT 12254029): RAG technique to generate response to a query.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHAHID AL ALAM whose telephone number is (571)272-4030. The examiner can normally be reached on M-F 8:00 AM-5:00 PM.
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January 10, 2026
/SHAHID A ALAM/Primary Examiner, Art Unit 2161