DETAILED ACTION
This action is responsive to claims filed on 13 June 2023.
Claims 1-20 are pending for examination.
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 Objections
Claim 1 and analogous claims 9, 17 are objected to because of the following informalities: “features store” in line 6 should be “feature store”. Appropriate correction is required.
Claim 7 and analogous claim 15 are objected to because of the following informalities: “features store” in lines 1-2 should be “feature store”. Appropriate correction is required.
Claim Rejections - 35 USC § 112
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.
Claim 3 is 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 3 recites the limitation "the category value" in lines 3-4. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, the term "the category value" has been construed to be “a category value”.
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 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because the full scope of “computer-readable storage medium” includes transitory signals or “signals per se”, See MPEP 2106.03.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception, abstract idea, without significantly more.
Step 1: This part of the eligibility analysis evaluates whether the claim(s) falls within any statutory
category. MPEP 2106.03:
According to the first part of the Alice analysis, in the instant case, the claims were determined
to be directed to one of the four statutory categories: an article of manufacture, a method/process (Claims 9-16), a machine/system/product (Claims 1-8), and a composition of matter. Based on the claims being determined to be within of the four categories (i.e., process, machine, manufacture, or composition of matter), (Step 1), it must be determined if the claims are directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea).
Step 2A Prong One: This part of the eligibility analysis evaluates whether the claim(s) recites a
judicial exception.
Regarding independent claims 1, 9, 17, the claims recite a judicial exception (i.e., an abstract idea enumerated in the 2019 PEG) without significantly more (Step-2A: Prong One). The applicant's claim limitations under broadest reasonable interpretation covers activities classified under mental processes - concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection Ill) and the 2019 PEG. As evaluated below:
Claims 1, 9, 17:
“query the feature store based on the query parameter, wherein the querying comprises identifying one or more vectors stored in the features store that match the query parameter via execution of a query on the feature store” (mental process of judgement)
If the identified limitation(s) falls within at least one of the groupings of abstract ideas, it is
reasonable to conclude that the claim(s) recites an abstract idea in Step 2A Prong One.
Step 2A Prong Two: This part of the eligibility analysis evaluates whether the claim(s) as a whole integrates the recited judicial exception into a practical application of the exception. As evaluated below:
“processor configured to receive a query parameter input via an interface of a software application”
“to generate a predicted output”
“display the predicted output via the interface of the software application"
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions for mere data gathering or data output, see MPEP 2106.05(g).
“execute a machine learning model on the one or more vectors identified in the feature store”
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
“a storage device comprising a feature store”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
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 when considered as an ordered combination and as a whole.
Step 2B: This part of the eligibility analysis evaluates whether the claim, as a whole, amounts to
significantly more than the recited exception, i.e., whether any additional element, or combination of
additional elements, adds an inventive concept to the claim. MPEP 2106.05.
First, the additional elements considered as part of the preamble and the additional elements
directed to the use of computer technology are deemed insufficient to transform the judicial exception
to a patentable invention to a patentable invention because they generally link the judicial exception to
the technology environment, see MPEP 2106.05(h).
Second, the additional elements directed to mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception, see MPEP 2106.05(f).
Third, the claims are directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception. The courts have found these types of limitations insufficient to transform the judicial exception to a patentable invention, see MPEP 2106.05(g).
Lastly, the claims directed to data gathering activity as noted above, are deemed directed to an insignificant extra-solution activity. The courts have found these types of limitations insufficient to
qualify as "significantly more", see MPEP 2106.05(g).
Furthermore, when considering evidence in view of Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018), see USPTO Berkheimer Memorandum (April 2018). Examiner notes Berkheimer: Option 2 - A citation to one or more of the court decisions discussed in MPEP § 2106.05(d}(II} as noting the well understood, routine, conventional nature of the additional element (s) (e.g., limitations directed to mere data gathering):
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, see MPEP 2106.05(d).
The additional limitations, as analyzed, failed to integrate a judicial exception into a practical application at Step 2A and provide an inventive concept in Step 2B, per the analysis above. Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible. Therefore, in examining elements as recited by the limitations individually and as an ordered combination, as a whole, claims 1, 9, 17 do not recite what the courts have identified as "significantly more".
Furthermore, regarding dependent claims 2-8, which depend from claim 1, claims 10-16, which depend from claim 9, claims 18-20, which depend from claim 17, the claims are directed to a judicial exception (i.e., an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon) without significantly more as highlighted below in the claim limitations by evaluating the claim limitations under the Step2A and 2B:
Claims 2, 10, 18:
Incorporates the rejections of claims 1, 9, 17, respectively.
“processor is configured to detect a selection of a category value via a drop-down menu of the interface”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
“identify the one or more vectors based on a comparison of the category value to respective keywords mapped to a plurality of vectors stored in the feature store” (mental process of judgement)
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Limitations directed to mere instructions to implement an abstract idea on a computer/using computer as a tool or directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 3:
Incorporates the rejection of claim 1.
“processor is further configured to detect a selection of a period of time via a drop-down menu of the interface”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
“execute the query against a plurality of vectors stored in the feature store based on a comparison of the category value to keywords of the plurality of vectors” (mental process of judgement)
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Limitations directed to mere instructions to implement an abstract idea on a computer/using computer as a tool or directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claims 4, 12, 20:
Incorporates the rejections of claims 1, 9, 17, respectively.
“processor is further configured to retrieve a plurality of vectors from the feature store based on the query parameter”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions for mere data gathering or data output, see MPEP 2106.05(g).
“execute the machine learning model on the plurality of vectors to train the machine learning model to perform a predictive function”
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Limitations directed to instructions for mere data gathering or data output or directed to mere instructions to implement an abstract idea on a computer/using computer as a tool cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claims 5, 13:
Incorporates the rejections of claims 1, 9, respectively.
“processor is further configured to receive a plurality of strings corresponding to a plurality of merchants”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions for mere data gathering or data output, see MPEP 2106.05(g).
“execute a second machine learning model on the plurality of strings to generate a plurality of merchant vectors corresponding to the plurality of merchants”
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
“store the plurality of merchant vectors in the feature store”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Limitations directed to instructions for mere data gathering or data output or directed to mere instructions to implement an abstract idea on a computer/using computer as a tool or directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claims 6, 14:
Incorporates the rejections of claims 5, 13, respectively.
“store the keywords within metadata of a merchant vector of the merchant within the feature store”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
“processor is further configured to identify keywords associated with a merchant from among the plurality of merchants” (mental process of judgement)
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Limitations directed to mere instructions to implement an abstract idea on a computer/using computer as a tool or directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claims 7, 15:
Incorporates the rejections of claims 1, 9, respectively.
“querying comprises querying the features store via an integrated development environment (IDE)”
“developing a machine learning model based on the one or more vectors identified in the feature store”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Limitations directed to mere instructions indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claims 8, 16:
Incorporates the rejections of claims 1, 9, respectively.
“processor is configured to compare attributes of the one or more vectors to a predefined criteria within vector space via execution of the machine learning model on the one or more vectors” (mental process of judgement)
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Limitations directed to mere instructions to implement an abstract idea on a computer/using computer as a tool cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claims 11, 19:
Incorporates the rejections of claims 9, 17, respectively.
“receiving comprises detecting a selection of a period of time via a drop-down menu of the interface”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
“querying comprises identifying the one or more vectors based on a comparison of the period of time to respective metadata of a plurality of vectors stored in the feature store” (mental process of judgement)
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Limitations directed to mere instructions to implement an abstract idea on a computer/using computer as a tool or directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
The dependent claims as analyzed above, do not recite limitations that integrated the judicial exception into a practical application. In addition, the claim limitations do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step-2B). Therefore, the claims do not recite any limitations, when considered individually or as a whole, that recite what have the courts have identified as "significantly more", see MPEP 2106.05; and therefore, as a whole the claims are not patent eligible. As shown above, the dependent claims do not provide any additional elements that when considered individually or as an ordered combination, amount to significantly more than the abstract idea identified. Therefore, as a whole, the dependent claims do not recite what have the courts have identified as "significantly more" than the recited judicial exception. Therefore, claims 2-8, 10-16, 18-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception and does not recite, when claim elements are examined individually and as a whole, elements that the courts have identified as "significantly more" than the recited judicial exception.
Claim Rejections - 35 USC § 102
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, 4-5, 8-9, 12-13, 16-17, 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Zhao et al. (U.S. Pre-Grant Publication No. 20230035639, hereinafter ‘Zhao').
Regarding claim 1 and analogous claims 9, 17, Zhao teaches An apparatus comprising ([0064] Embodiments of the invention may be implemented on a computing system. Any combination of mobile, desktop, server, router, switch, embedded device, or other types of hardware may be used. For example, as shown in FIG. 5A, the computing system (500) may include one or more computer processors (502), non-persistent storage (504) (e.g., volatile memory, such as random access memory (RAM), cache memory), persistent storage (506) (e.g., a hard disk, an optical drive such as a compact disk (CD) drive or digital versatile disk (DVD) drive, a flash memory, etc.), a communication interface (512) (e.g., Bluetooth interface, infrared interface, network interface, optical interface, etc.), and numerous other elements and functionalities.; [0065] The computer processor(s) (502) may be an integrated circuit for processing instructions. For example, the computer processor(s) may be one or more cores or micro-cores of a processor. The computing system (500) may also include one or more input devices (510), such as a touchscreen, keyboard, mouse, microphone, touchpad, electronic pen, or any other type of input device.):
a storage device comprising a feature store ([0023] The a storage device repository (122) is a computing system that may include multiple computing devices in accordance with the computing system (500) and the nodes (522) and (524) described below in FIGS. 5A and 5B. The repository (122) may be hosted by a cloud services provider that also hosts the server (110). The cloud services provider may provide hosting, virtualization, and data storage services as well as other cloud services and to operate and control the data, programs, and applications that store and retrieve data from the repository (122). The data in the repository (122) includes untransformed transactions (123), transformed transactions (124), comprising a feature store a feature store (126), training data (128), and, optionally, a vector store (130).); and
a processor configured to receive a query parameter input via an interface of a software application ([0080] The computing system in FIG. 5A may implement and/or be connected to a data repository. For example, one type of data repository is a database. A database is a collection of information configured for ease of data retrieval, modification, reorganization, and deletion. via an interface of a software application Database Management System (DBMS) is a software application that provides an interface for users to define, create, query, update, or administer databases.; [0081] The a processor configured to receive a query parameter input user, or software application, may submit a statement or query into the DBMS. Then the DBMS interprets the statement. The statement may be a select statement to request information, update statement, create statement, delete statement, etc. Moreover, the statement may include parameters that specify data, or data container (database, table, record, column, view, etc.), ID(s), conditions (comparison operators), functions (e.g. join, full join, count, average, etc.), sort (e.g. ascending, descending), or others. The DBMS may execute the statement. For example, the DBMS may access a memory buffer, a reference or index a file for read, write, deletion, or any combination thereof, for responding to the statement. The DBMS may load the data from persistent or non-persistent storage and perform computations to respond to the query. The DBMS may return the result(s) to the user or software application.),
query the feature store based on the query parameter ([0024] The feature store (126) includes features derived from transformed transactions (124), as described in FIG. 1C below. The training data (128) includes data used to train the machine learning models (114) of the system (100). The training data may include training inputs and labels. The vector store (130) includes vectors generated from untransformed transactions (123), as described in FIG. 1D below.; [0037] Turning to FIG. 1C, the query generator (115) includes functionality to query the feature store based on the query parameter generate a query (166) from an untransformed transaction (140) and/or cluster IDs (162, 163), as described in Step 256 of FIG. 2 below.),
wherein the querying comprises identifying one or more vectors stored in the features store that match the query parameter via execution of a query on the feature store ([0053] In Step 260, wherein the querying comprises identifying one or more vectors stored in the features store that match the query parameter via execution of a query on the feature store a query result is generated, using the query, from features of the transformed transactions. For example, an expression in the query may specify a cluster ID to be used as a search criterion to be matched against the features (e.g., the cluster-derived features) of the transformed transactions.);
execute a machine learning model on the one or more vectors identified in the feature store to generate a predicted output ([0022] The machine learning models (114) are programs running as part of the server application (112). The execute a machine learning model machine learning models (114) include the embedding models (148, 149), cluster models (160, 161), and the fraud determination model (172) of FIG. 1B.; [0041] The fraud determination model (172) includes functionality to on the one or more vectors identified in the feature store to generate a predicted output generate, using the query (166), a fraud score (174) from features of transformed transactions (124) (e.g., cluster-derived features (168) and raw features (170) included in the feature store (126)). For example, the result of executing the query (166) may be a numerical value generated using an aggregation operator.), and
display the predicted output via the interface of the software application ([0080] The computing system in FIG. 5A may implement and/or be connected to a data repository. For example, one type of data repository is a database. A database is a collection of information configured for ease of data retrieval, modification, reorganization, and deletion. Database Management System (DBMS) is a of the software application software application that provides an interface for users to define, create, query, update, or administer databases.; [0082] The computing system of FIG. 5A may include functionality to present raw and/or processed data, such as results of comparisons and other processing. For example, presenting data may be accomplished through various presenting methods. Specifically, data may be presented through a user interface provided by a computing device. The user interface may include a display the predicted output via the interface GUI that displays information on a display device, such as a computer monitor or a touchscreen on a handheld computer device.).
Regarding claim 4 and analogous claims 12, 20, Zhao teaches The apparatus of claim 1, The method of claim 9, and The computer-readable storage medium of claim 17, respectively.
Zhao teaches wherein the processor is further configured to retrieve a plurality of vectors from the feature store based on the query parameter, and execute the machine learning model on the plurality of vectors to train the machine learning model to perform a predictive function ([0022] The machine learning models (114) are programs running as part of the server application (112). The execute the machine learning model machine learning models (114) include the embedding models (148, 149), cluster models (160, 161), and the fraud determination model (172) of FIG. 1B.; [0041] The fraud determination model (172) includes functionality to to perform a predictive function generate, based on the query parameter using the query (166), a fraud score (174) from configured to retrieve a plurality of vectors from the feature store features of transformed transactions (124) (e.g., cluster-derived features (168) and raw features (170) included in the feature store (126)). For example, the result of executing the query (166) may be a numerical value generated using an aggregation operator.; [0042] The fraud determination model (172) may be on the plurality of vectors to train the machine learning model trained using untransformed transactions and different combinations of features (e.g., cluster-derived features and/or raw features) derived from the training transactions. Each training transaction may be labeled as “fraudulent” or “valid.” By decoupling the embedding models (148, 149) from fraud determination model (172) training and operation, the overall development lifecycle may be accelerated while maintaining the same level of runtime performance.).
Regarding claim 5 and analogous claim 13, Zhao teaches The apparatus of claim 1, The method of claim 9, respectively.
Zhao teaches wherein the processor is further configured to receive a plurality of strings corresponding to a plurality of merchants ([0035] The receive a plurality of strings corresponding to a plurality of merchants cluster ID may be a unique ID (e.g., an integer or alphanumeric string). Each cluster ID may correspond to an intent (e.g., a pattern, or a purpose) of unstructured data corresponding to the vectors in the cluster identified by the cluster ID (162).),
execute a second machine learning model on the plurality of strings to generate a plurality of merchant vectors corresponding to the plurality of merchants, and store the plurality of merchant vectors in the feature store ([0039] The feature generator (116) includes functionality to execute a second machine learning model to generate a plurality of merchant vectors corresponding to the plurality of merchants derive features from transformed transactions (124). The feature generator (116) includes functionality to store the plurality of merchant vectors in the feature store store the derived features in the feature store (126). Features may be derived by executing one or more queries that access the transformed transactions (124). For example, the queries may access the on the plurality of strings cluster IDs of the transformed transactions (124). The features may be derived from multiple entities in the transformed transactions (124). For example, the multiple entities may include: a merchant, a customer, a bank account, and/or a payment.).
Regarding claim 8 and analogous claim 16, Zhao teaches The apparatus of claim 1, The method of claim 9, respectively.
Zhao teaches wherein the processor is configured to compare attributes of the one or more vectors to a predefined criteria within vector space via execution of the machine learning model on the one or more vectors ([0015] An embedding model is applied to the unstructured data of an untransformed transaction to generate a vector. A cluster ID is assigned to the vector by matching the vector with a cluster of vectors. The cluster ID may identify a cluster of vectors that are within a threshold distance of a centroid. For example, a cluster ID corresponding to “invoice memo” unstructured data may correspond to “utilities,” indicating that the untransformed transaction including the invoice memo corresponds to a utilities expense.; [0034] The cluster models (160, 161) include functionality to assign cluster IDs (IDs) (162, 163) to vectors (152, 153). The cluster models (160, 161) may correspond to different unstructured data (141, 142). For example, cluster model (160) may correspond to unstructured data (141) and cluster model (161) may correspond to unstructured data (142). A cluster ID (162) configured to compare attributes of the one or more vectors to a predefined criteria within vector space via execution of the machine learning model on the one or more vectors identifies a cluster of vectors that are within a threshold distance of a centroid (e.g., center point) of the cluster of vectors. For example, the distance may be based on a cosine similarity or Euclidean distance between vectors. Continuing this example, the centroid may be a point (e.g., a vector) that represents an average of the vectors in the cluster. The cluster models (160, 161) may group vectors (152, 153) into clusters using various techniques, such as k-means clustering and Density-Based Spatial Clustering of Applications with Noise (DBSCAN).).
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.
The factual inquiries 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.
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 2-3, 6, 10-11, 14, 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Zhao, in view of Kaczynski et al. (U.S. Pre-Grant Publication No. 20220414541, hereinafter 'Kaczynski').
Regarding claim 2 and analogous claims 10, 18, Zhao teaches The apparatus of claim 1, The method of claim 9, and The computer-readable storage medium of claim 17.
Zhao fails to teach wherein the processor is configured to detect a selection of a category value via a drop-down menu of the interface, and identify the one or more vectors based on a comparison of the category value to respective keywords mapped to a plurality of vectors stored in the feature store.
Kaczynski teaches wherein the processor is configured to detect a selection of a category value via a drop-down menu of the interface, and identify the one or more vectors based on a comparison of the category value to respective keywords mapped to a plurality of vectors stored in the feature store ([0108] Within the application, a user may interact with one or more user interface windows presented to the user in a display under control of the ESPE independently or through a browser application in an order selectable by the user. For example, a user may execute an ESP application, which causes processor is configured to detect a selection of a category value via a drop-down menu of the interface presentation of a first user interface window, which may include a plurality of menus and selectors such as drop down menus, buttons, text boxes, hyperlinks, etc. associated with the ESP application as understood by a person of skill in the art. As further understood by a person of skill in the art, various operations may be performed in parallel, for example, using a plurality of threads.; [0165] A fourth operation comprises Feature Engineering 1518. For instance, a user such as an Analyst using the of the category value to respective keywords mapped to a plurality of vectors stored in the feature store Feature Store 1512 can define new variables or select definitions from among those available in a repository based on the results of the previous steps, i.e., and identify the one or more vectors based on a comparison extending the set of features and aggregates used to estimate model parameters. Variables can be defined in a variety of forms (e.g., both in the form of code as well as in the form of diagrams such as SAS Event Stream Processing diagrams, especially for events).; [0169] A production system implementing a Production Environment 1560 can be used to store the generated features (Variables 1562) and developed Models 1580. The Variables 1562 and Models 1580 can be deployed in the Production Environment 1560. The Variables 1562 can be used or generated using a variety of engines such as relational database (e.g., in-database 1564), real-time (streaming) engine 1566, or CAS® in-memory engine 1568. Similarly, the models can be scored using various engines (e.g., in-database 1582), in real-time engine 1584, or CAS® engine 1586. A searchable database can provide a unified view of the data (e.g., a unified approach to evaluating historical data for training and scoring a model). In creating new features and variables, where there is no unified way to evaluate historical and production data, data scientists do this in their own ad hoc manner. A feature storage 1312 can provide for preconfigured feature sets (e.g., in a single or multiple searchable metadata repositories). The database can be used to track information providing an auditable process and data lineage.).
Zhao and Kaczynski are considered to be analogous to the claimed invention because they are in the same field of machine learning. In view of the teachings of Zhao, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Kaczynski to Zhao before the effective filing date of the claimed invention in order to determine the best way to structure (e.g., hierarchically) data, such that the structured data is tailored to a type of further analysis that a user wishes to perform on the data (cf. Kaczynski, [0039] The unstructured data may be presented to the computing environment 114 in different forms such as a flat file or a conglomerate of data records, and may have data values and accompanying time stamps. The computing environment 114 may be used to analyze the unstructured data in a variety of ways to determine the best way to structure (e.g., hierarchically) that data, such that the structured data is tailored to a type of further analysis that a user wishes to perform on the data.).
Regarding claim 3, Zhao teaches The apparatus of claim 1.
Zhao fails to teach wherein the processor is further configured to detect a selection of a period of time via a drop-down menu of the interface, and in response, execute the query against a plurality of vectors stored in the feature store based on a comparison of the category value to keywords of the plurality of vectors.
Kaczynski teaches wherein the processor is further configured to detect a selection of a period of time via a drop-down menu of the interface, and in response, execute the query against a plurality of vectors stored in the feature store based on a comparison of the category value to keywords of the plurality of vectors ([0108] Within the application, a user may interact with one or more user interface windows presented to the user in a display under control of the ESPE independently or through a browser application in an order selectable by the user. For example, a user may execute an ESP application, which causes wherein the processor is further configured to via a drop-down menu of the interface presentation of a first user interface window, which may include a plurality of menus and selectors such as drop down menus, buttons, text boxes, hyperlinks, etc. associated with the ESP application as understood by a person of skill in the art. As further understood by a person of skill in the art, various operations may be performed in parallel, for example, using a plurality of threads.; [0150] Additionally, or alternatively, the input/output interface(s) 1304 can be used to receive a requested data set for an input variable for developing an analytical model. For example, there may be a particular time period or other range of interest for input data for a variable, and a scientist may detect a selection of a period of time request that time period or range for an existing feature.; [0165] A fourth operation comprises Feature Engineering 1518. For instance, a user such as an Analyst using the Feature Store 1512 can define new variables or select definitions from among those available in a repository based on the results of the previous steps, i.e., extending the set of features and aggregates used to estimate model parameters. Variables can be defined in a variety of forms (e.g., both in the form of code as well as in the form of diagrams such as SAS Event Stream Processing diagrams, especially for events).; [0169] A production system implementing a Production Environment 1560 can be used to store the generated features (Variables 1562) and developed Models 1580. The Variables 1562 and Models 1580 can be deployed in the Production Environment 1560. The Variables 1562 can be used or generated using a variety of engines such as relational database (e.g., in-database 1564), real-time (streaming) engine 1566, or CAS® in-memory engine 1568. Similarly, the models can be scored using various engines (e.g., in-database 1582), in real-time engine 1584, or CAS® engine 1586. in response, execute the query against a plurality of vectors stored in the feature store based on a comparison of the category value to keywords of the plurality of vectors A searchable database can provide a unified view of the data (e.g., a unified approach to evaluating historical data for training and scoring a model). In creating new features and variables, where there is no unified way to evaluate historical and production data, data scientists do this in their own ad hoc manner. A feature storage 1312 can provide for preconfigured feature sets (e.g., in a single or multiple searchable metadata repositories). The database can be used to track information providing an auditable process and data lineage.).
Zhao and Kaczynski are combinable for the same rationale as set forth above with respect to claim 2.
Regarding claim 6 and analogous claim 14, Zhao teaches The apparatus of claim 5, The method of claim 13.
Zhao fails to teach wherein the processor is further configured to identify keywords associated with a merchant from among the plurality of merchants, and store the keywords within metadata of a merchant vector of the merchant within the feature store.
Kaczynski teaches wherein the processor is further configured to identify keywords associated with a merchant from among the plurality of merchants, and store the keywords within metadata of a merchant vector of the merchant within the feature store ([0169] A feature storage 1312 can provide for preconfigured feature sets (e.g., in a single or multiple searchable metadata repositories). The database can be used to track information providing an auditable process and data lineage.; [0170] The store the keywords within metadata of a merchant vector of the merchant within the feature store Feature Store 1512 uses an innovative approach to managing metadata of available data used for model development as well as model execution. The approach allows for defining both real-time event data and batch evaluations which is framework-agnostic.; [0174] In this example, the computing system associates a first preconfigured feature set (e.g., Feature Set 1710) and a second preconfigured wherein the processor is further configured to identify keywords associated with a merchant feature set (e.g., Feature Set 1720) associated with an entity (e.g., Entity 1740). The from among the plurality of merchants entity can represent, for example, a real-world object, event, person, or business. For example, as shown in FIG. 16A, the entities included clients, transactions and contracts. In one or more embodiments, a computing system generates requested data set (e.g., where the data set is not already available) by generating an analytical data set (e.g., Analytical Set 1750) for the entity comprising data pertaining to each of the first preconfigured feature set and the second preconfigured feature set. For example, calculation results from a specific period 1730 are used for each of feature set 1710 and feature set 1720, but data from other periods such as periods 1732 and 1734 are excluded. The Analytical Set 1750 can then have its own features 1752 and may be associated with the entity 1740. The entity may have other metadata 1742 associated with it such as pertaining to a key, time, or partition.).
Zhao and Kaczynski are combinable for the same rationale as set forth above with respect to claim 2.
Regarding claim 11 and analogous claim 19, Zhao teaches The method of claim 9, The computer-readable storage medium of claim 17.
Zhao fails to teach wherein the receiving comprises detecting a selection of a period of time via a drop-down menu of the interface, and the querying comprises identifying the one or more vectors based on a comparison of the period of time to respective metadata of a plurality of vectors stored in the feature store.
Kaczynski teaches wherein the receiving comprises detecting a selection of a period of time via a drop-down menu of the interface, and the querying comprises identifying the one or more vectors based on a comparison of the period of time to respective metadata of a plurality of vectors stored in the feature store ([0108] Within the application, a user may interact with one or more user interface windows presented to the user in a display under control of the ESPE independently or through a browser application in an order selectable by the user. For example, a user may execute an ESP application, which causes receiving comprises via a drop-down menu of the interface presentation of a first user interface window, which may include a plurality of menus and selectors such as drop down menus, buttons, text boxes, hyperlinks, etc. associated with the ESP application as understood by a person of skill in the art. As further understood by a person of skill in the art, various operations may be performed in parallel, for example, using a plurality of threads.; [0150] Additionally, or alternatively, the input/output interface(s) 1304 can be used to receive a requested data set for an input variable for developing an analytical model. For example, there may be a particular time period or other range of interest for input data for a variable, and a scientist may detecting a selection of a period of time request that time period or range for an existing feature.; [0169] querying comprises identifying the one or more vectors A searchable database can provide a unified view of the data (e.g., a unified approach to evaluating historical data for training and scoring a model). In creating new features and variables, where there is no unified way to evaluate historical and production data, data scientists do this in their own ad hoc manner. A feature storage 1312 can provide for preconfigured feature sets (e.g., in a single or multiple searchable metadata repositories). The database can be used to track information providing an auditable process and data lineage.; [0180] This absence of appropriate metadata can be used to determine whether the requested data set is available for retrieval. For instance, the presence or absence of needed metadata may be an implicit indication of availability status. For example, the available based on a comparison of the period of time to respective metadata of a plurality of vectors stored in the feature store data may be specific to a particular time period and the requested data set is requested for a time that is different (e.g., in overlapping time period or in a time period that does not include the particular time period available). For instance, if a computing system stores in metadata that calculations were performed for data received in January, if another computing system asks for February, a computing system will execute evaluations based on the definition for this period and information stored in metadata since the metadata indicates that only January is available. In other words, the computing system has no computer instructions available for locating the requested data set stored, or set-up to arrive, in the feature storage, and must determine or extrapolate the data needed.).
Zhao and Kaczynski are combinable for the same rationale as set forth above with respect to claim 2.
Claims 7, 15 are rejected under 35 U.S.C. 103 as being unpatentable over Zhao, in view of Nothaft et al. (U.S. Pre-Grant Publication No. 20230187074, hereinafter 'Nothaft').
Regarding claim 7 and analogous claim 15, Zhao teaches The apparatus of claim 1, The method of claim 9.
Zhao fails to teach wherein the querying comprises querying the features store via an integrated development environment (IDE), and developing a machine learning model based on the one or more vectors identified in the feature store.
Nothaft teaches wherein the querying comprises querying the features store via an integrated development environment (IDE), and developing a machine learning model based on the one or more vectors identified in the feature store ([0065] The interactive analysis portal 22 may include a plurality of user interfaces including an interactive cohort selection filtering interface 24 that, as discussed in greater detail below, querying comprises permits a user to query and filter elements of the data store 14.; [0089] querying the features store A feature store may enhance a patient's feature set through the application of machine learning and analytics by selecting from any features, alterations, or calculated output derived from the patient's features or alterations to those features. Such a feature store may generate new features from the original features found in feature module or may identify and store important insights or analysis based upon the features.; [0383] The environments section 4114, as shown, can display information about one or multiple technological environments 4118 associated with the workspace. Each of the one of more environments 4118 can be defined by technological resources, including computing infrastructure of the environment, tools or services associated with the environment, and an via an integrated development environment (IDE) integrated development environments (IDE) through which the user can program workloads within the workspace.; [0091] Artificial intelligence models referenced herein may be gradient boosting models, random forest models, neural networks (NN), regression models, Naive Bayes models, or machine learning algorithms (MLA). A developing a machine learning model based on the one or more vectors identified in the feature store MLA or a NN may be trained from a training data set. In an exemplary prediction profile, a training data set may include imaging, pathology, clinical, and/or molecular reports and details of a patient, such as those curated from an EHR or genetic sequencing reports. MLAs include supervised algorithms (such as algorithms where the features/classifications in the data set are annotated) using linear regression, logistic regression, decision trees, classification and regression trees, Naïve Bayes, nearest neighbor clustering; unsupervised algorithms (such as algorithms where no features/classification in the data set are annotated) using Apriori, means clustering, principal component analysis, random forest, adaptive boosting; and semi-supervised algorithms (such as algorithms where an incomplete number of features/classifications in the data set are annotated) using generative approach (such as a mixture of Gaussian distributions, mixture of multinomial distributions, hidden Markov models), low density separation, graph-based approaches (such as mincut, harmonic function, manifold regularization), heuristic approaches, or support vector machines.).
Zhao and Nothaft are considered to be analogous to the claimed invention because they are in the same field of machine learning. In view of the teachings of Zhao, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Nothaft to Zhao before the effective filing date of the claimed invention in order to facilitate the discovery of insights of therapeutic significance, through the automated analysis of patterns occurring in patient clinical, molecular, phenotypic, and response data, and enabling further exploration via a fully integrated, reactive user interface (cf. Nothaft, [0006] The system described herein facilitates the discovery of insights of therapeutic significance, through the automated analysis of patterns occurring in patient clinical, molecular, phenotypic, and response data, and enabling further exploration via a fully integrated, reactive user interface.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Boue et al. (U.S. Pre-Grant Publication No. 20240020283) teaches methods, systems, apparatuses, and computer-readable storage mediums for identifying a similarity between queries.
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/MM/Examiner, Art Unit 2129
/MICHAEL J HUNTLEY/Supervisory Patent Examiner, Art Unit 2129