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 .
Claim Rejections - 35 USC § 102
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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 4-6, 8-9, 12-14, 16, 19 are rejected under 35 U.S.C. 102(a)(1)/102(a)(2) as being anticipated by Mavinakuli (US 2023/0012316).
Regarding Claims 1, 9, and 8, Mavinakuli discloses a computing system, comprising:
one or more processor devices (Fig. 1, 100, 160A, 160B, Mavinakuli);
one or more tangible, non-transitory computer-readable media that store instructions that, when executed by the one or more processor devices, cause the one or more processor devices to perform operations, the operations comprising (Fig. 1, 100, 160A, 160B, Mavinakuli):
receiving a plurality of vectorization requests via a plurality of active connections for a hybrid time-series vector database, wherein each of the plurality of vectorization requests comprises an input of a corresponding plurality of inputs, and wherein of the plurality of inputs comprises ([0063], a request, Mavinakuli):
information associated with an event ([0063], “determines, using a trained machine-learning model and the human-readable document, a name of a person making a request for leave and a date of the requested leave,” name; Mavinakuli); and
temporal information indicative of a time at which the event occurred ([0063], “determines, using a trained machine-learning model and the human-readable document, a name of a person making a request for leave and a date of the requested leave,” date; Mavinakuli);
processing the plurality of inputs with a machine-learned vector embedding model to generate a corresponding vector representation of a plurality of vector representations, wherein a temporal portion of the vector representation represents the temporal information ([0063], “may be converted to a vector representation using a language embedder,” Mavinakuli);
for each of the plurality of vector representations, mapping the vector representation to a corresponding location of a plurality of locations within an embedding portion of the hybrid time-series vector database based at least in part on the temporal portion of the vector representation ([0057], Fig. 5, 500, Fig. 6, 650, [0067], “The leave management system 150, in operation 650, in response to the received confirmation, stores the determined name of the person and the determined date of the requested leave in a database. For example, a record may be added to the leave request table 540 of FIG. 5. Thus, by use of the method 600, a leave request is made and both a computer-readable database is updated and a human-readable document is sent without requiring a user to both send a human-readable document to a manager or peers and to initiate and complete a computer-readable form,” wherein storing and adding a record in the table corresponds to the mapping as claimed; wherein the leave request table corresponds to the hybrid time-series vector database as claimed; Mavinakuli);
receiving a query for the hybrid time-series vector database via an active connection of the plurality of active connections ([0074]-[0075], “a user may change the initially populated values, add values…,” Mavinakuli); and
responsive to the query, retrieving a first vector representation of the plurality of vector representations based at least in part on the location to which the first vector representation is mapped ([0076], “the data in any updated data fields is provided to the leave management system 150,” Mavinakuli).
Regarding Claims 4 and 12, Mavinakuli discloses one or more tangible, non-transitory computer-readable media, wherein the operations further comprise:
receiving a data storage request via the active connection of the plurality of active connections, wherein the active storage request comprises textual content for storage to the hybrid time-series vector database (Fig. 8, 820-870, Mavinakuli); and
storing a data entry to a non-embedding portion of the hybrid time-series database, wherein the data entry comprises the textual content (Fig. 8, 820-870, Mavinakuli).
Regarding Claims 5 and 13, Mavinakuli discloses one or more tangible, non-transitory computer-readable media, wherein the operations further comprise:
storing historical information descriptive of the query and the first vector representation to the hybrid vector database ([0059]-[0060] and Fig. 5, 540-560C, Mavinakuli).
Regarding Claims 6 and 14, Mavinakuli discloses one or more tangible, non-transitory computer-readable media, wherein the operations further comprise:
receiving a second query via the active connection of the plurality of active connections ([0059]-[0060] and Fig. 5, 540-560C, Mavinakuli); and
responsive to the query, retrieving a second vector representation of the plurality of vector representations based at least in part on the historical information and the location to which the second vector representation is mapped ([0059]-[0060] and Fig. 5, 540-560C, Mavinakuli).
Regarding Claim 16, Mavinakuli discloses one or more tangible, non-transitory computer-readable media of claim 9, wherein the method further comprises: processing a set of inputs with a machine-learned model to obtain a model output, wherein the set of inputs comprises:
(a) the vector representation or information derived from the vector representation ([0059]-[0060] and Fig. 5, 540-560C, Mavinakuli); and
(b) the query or a vector representation of the query ([0059]-[0060] and Fig. 5, 540-560C, Mavinakuli); and
wherein the machine-learned model is trained to process a query and a set of contextual information to generate a generative output, wherein the generative output is responsive to the query, and wherein the generative output is conditioned on the contextual information ([0081], [0090], and [0098], Mavinakuli).
Regarding Claim 19, Mavinakuli discloses one or more tangible, non-transitory computer-readable media of claim 9, wherein the method further comprises:
processing one or more inputs with a machine-learned model to obtain a model output, wherein the one or more inputs comprises at least one of ([0063], “may be converted to a vector representation using a language embedder,” Mavinakuli):
(a) the vector representation ([0063], “may be converted to a vector representation using a language embedder,” Mavinakuli); or
(b) information represented by the vector representation ([0063], “may be converted to a vector representation using a language embedder,” Mavinakuli); and
wherein the machine-learned model is trained to process information descriptive of a state of a particular industry to generate an industry-specific prediction output ([0014], [0016], and [0053], Mavinakuli).
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 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.
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.
Claims 2-3, 7, and 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Mavinakuli (US 2023/0012316) in view of Bagherinezhad et al. (US 2020/0387783).
Regarding Claims 2 and 10, Mavinakuli discloses all the limitations as discussed above including: wherein retrieving the first vector representation of the plurality of vector representations comprises: processing the query with the machine-learned vector embedding model to generate a query vector representation ([0063], “may be converted to a vector representation using a language embedder,” Mavinakuli); and based on the query vector representation, performing a search with the hybrid time-series vector database to retrieve the first vector representation ([0074]-[0075], “a user may change the initially populated values, add values…,” and [0076], “the data in any updated data fields is provided to the leave management system 150,” Mavinakuli). However, Mayinakuli does not expressly disclose performing a nearest-neighbor search. Bagherinezhad discloses performing a nearest-neighbor search ([0030] and [0041], Bagherinezhad). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the system of Mayinakuli by incorporating the performing a nearest-neighbor search, as disclosed by Bagherinezhad, in order to improve the computational efficiency without any drop in the accuracy ([0041], Bagherinezhad). See: KSR International Co. v. Teleflex Inc., 82 USPQ 1385, 1396 (US 2007); MPEP § 2143.
Regarding Claims 3 and 11, Mayinakuli/Bagherinezhad discloses one or more tangible, non-transitory computer-readable media, wherein the operations further comprise:
processing the query vector representation and the first vector representation with a machine-learned generative model to obtain a generative output ([0098] and [0121]-[0122], Mavinakuli); and
providing the generative output to via the active connection of the plurality of active connections ([0098] and [0121]-[0122], Mavinakuli).
Regarding Claim 7, Mayinakuli/Bagherinezhad discloses a method of claim 1, wherein the information associated with the event comprises a plurality of sensor measurements collected during a sensor activation event.
Claims 15, 17-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Mavinakuli (US 2023/0012316).
Regarding Claim 15, Mavinakuli discloses all the limitations as discussed above including: processing one or more inputs with a machine-learned model to obtain a model output, wherein the one or more inputs comprises at least one of ([0054] and [0063], Mavinakuli):
(a) the vector representation ([0054] and [0063], Mavinakuli); or
(b) information represented by the vector representation; and wherein the machine-learned model is trained to process information indicative of transactions to generate output ([0054] and [0063], Mavinakuli).
However, Mavinakuli does not expressly disclose a fraud detection output. However, these differences are only found in the nonfunctional descriptive material and is not functionally involved in the steps recited. The processing steps would be performed the same regardless of the type of output. Thus, this descriptive material will not distinguish the claimed invention from the prior art in terms of patentability, see In re Gulack, 703 F.2d 1381, 1385, 217 USPQ 401, 404 (Fed. Cir. 1983); In re Lowry, 32 F.3d 1579, 32 USPQ2d 1031 (Fed. Cir. 1994).
Therefore, it would have been obvious to a person of ordinary skill in the art at the time the invention was made to include any type of data in the output because such data does not functionally relate to the steps in the method claimed and because the subjective interpretation of the data does not patentably distinguish the claimed invention.
Regarding Claim 17, Mayinakuli discloses all the limitations as discussed above including:
processing one or more inputs with a machine-learned model to obtain a model output, wherein the one or more inputs comprises at least one of ([0054] and [0063], Mavinakuli):
(a) the vector representation ([0054] and [0063], Mavinakuli); or
(b) information represented by the vector representation ([0054] and [0063], Mavinakuli); and
wherein the machine-learned model is trained to process information indicative to generate a predictive output ([0054] and [0063], Mavinakuli).
However, Mavinakuli does not expressly disclose information indicative of satellite imagery. However, these differences are only found in the nonfunctional descriptive material and is not functionally involved in the steps recited. The processing steps would be performed the same regardless of the type of information. Thus, this descriptive material will not distinguish the claimed invention from the prior art in terms of patentability, see In re Gulack, 703 F.2d 1381, 1385, 217 USPQ 401, 404 (Fed. Cir. 1983); In re Lowry, 32 F.3d 1579, 32 USPQ2d 1031 (Fed. Cir. 1994).
Therefore, it would have been obvious to a person of ordinary skill in the art at the time the invention was made to include any type of information because such information does not functionally relate to the steps in the method claimed and because the subjective interpretation of the information does not patentably distinguish the claimed invention.
Regarding Claim 18, Mayinakuli discloses one or more tangible, non-transitory computer-readable media of claim 9, wherein the method further comprises:
processing one or more inputs with a machine-learned model to obtain a model output, wherein the one or more inputs comprises at least one of ([0054] and [0063], Mavinakuli):
(a) the vector representation ([0054] and [0063], Mavinakuli); or
(b) information represented by the vector representation ([0054] and [0063], Mavinakuli); and
wherein the machine-learned model is trained to process information indicative of readings to generate a prediction output ([0054] and [0063], Mavinakuli).
However, Mavinakuli does not expressly disclose agricultural sensor readings and agricultural prediction output. However, these differences are only found in the nonfunctional descriptive material and is not functionally involved in the steps recited. The processing steps would be performed the same regardless of the type of readings and the type of output. Thus, this descriptive material will not distinguish the claimed invention from the prior art in terms of patentability, see In re Gulack, 703 F.2d 1381, 1385, 217 USPQ 401, 404 (Fed. Cir. 1983); In re Lowry, 32 F.3d 1579, 32 USPQ2d 1031 (Fed. Cir. 1994).
Therefore, it would have been obvious to a person of ordinary skill in the art at the time the invention was made to include any type of readings and type of output because such data does not functionally relate to the steps in the method claimed and because the subjective interpretation of the data does not patentably distinguish the claimed invention.
Regarding Claim 20, Mavinakuli discloses all the limitations as discussed above including:
processing one or more inputs with a machine-learned model to obtain a model output, wherein the one or more inputs comprises at least one of ([0054] and [0063], Mavinakuli):
(a) the vector representation ([0054] and [0063], Mavinakuli); or
(b) information represented by the vector representation ([0054] and [0063], Mavinakuli); and
wherein the machine-learned model is trained to process information to generate a model output, wherein the model output comprises ([0054] and [0063], Mavinakuli):
information ([0054] and [0063], Mavinakuli).
However, Mavinakuli does not expressly disclose health-related information, information indicative of predicted drug compound, or information indicative of predicted epidemic outbreak, information indicative of one or more predicted treatments for a particular user. However, these differences are only found in the nonfunctional descriptive material and is not functionally involved in the steps recited. The processing steps would be performed the same regardless of the type of information or output. Thus, this descriptive material will not distinguish the claimed invention from the prior art in terms of patentability, see In re Gulack, 703 F.2d 1381, 1385, 217 USPQ 401, 404 (Fed. Cir. 1983); In re Lowry, 32 F.3d 1579, 32 USPQ2d 1031 (Fed. Cir. 1994).
Therefore, it would have been obvious to a person of ordinary skill in the art at the time the invention was made to include any type of information or output because such information does not functionally relate to the steps in the method claimed and because the subjective interpretation of the information does not patentably distinguish the claimed invention.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GIOVANNA B COLAN whose telephone number is (571)272-2752. The examiner can normally be reached Mon - Fri 8:30-5:00.
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, Aleksandr Kerzhner can be reached at (571) 270-1760. 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.
/GIOVANNA B COLAN/Primary Examiner, Art Unit 2165 January 10, 2026