DETAILED ACTION
1. Claims 1-20 are pending in this application.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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.
Claim Interpretation
3. The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “an outflow module comprising logic to” and “user interface logic to” in claim 1.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
4. 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.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claims 1 and 11 recite “presenting the visualizations and the anchor tags to a user for selection of the anchor tag;” However, it is unclear what is being presented because the claims do not specify what the visualization represents or what it consists of. Therefore, the claims are indefinite.
Dependent claims 2-10 and 12-20 are also rejected for inheriting the deficiencies of the base claims.
Claims 1 and 11 recite “initiating the resource management operations based on the time-recurrent cluster monitoring signal, thereby improving operational efficiencies of at least one of the ingest module and the outflow module by improving at least one of communication and operational bandwidth, system stability, and latency between the ingest module and the outflow module.” However, it is unclear what operations are being initiated based on the generated cluster monitoring signal. Therefore, the claims are indefinite.
Dependent claims 2-10 and 12-20 are also rejected for inheriting the deficiencies of the base claims.
Claims 2 and 12 recite “over time” however it is unclear and indefinite how much time represents a period of time that constitutes as over time.
Claim Rejections - 35 USC § 101
5. 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 an abstract idea (Mental Process) without significantly more. The claims similarly describe methods for detecting patterns in recurring resource management operations, enabling systems to anticipate and optimize future resource usage.
The following is an analysis based on 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG).
Step 1, Statutory Category?
Claims 1–10 are directed to a system and, therefore, fall within at least one of the four statutory categories.
Claims 11–20 are directed to a method and, therefore, fall within at least one of the four statutory categories.
Step 2A, Prong I: Judicial Exception Recited?
The examiner submits that the foregoing claim limitations constitute a “Mental Process”, as the claims cover performance of the limitations in the human mind, given the broadest reasonable interpretation.
As per claims 1 and 11, the claims similarly recite the limitations of:
“an outflow module comprising logic to transform the digital records from the disparate computer server systems into visualizations and anchor tags by:” A human can observe a data record and mentally create a visualizing of the observed record; for example, when a human looks at monthly sales numbers and imagines a bar chart in their mind, picturing taller bars for higher sales months and shorter bars for lower ones to quickly identify trends and anomalies without seeing a physical chart. A human can also can mentally create an “anchor tag” by linking a specific, simple word, phrase, image, or touch to a strong feeling or memory, allowing you to recall that state later; for example, a person feeling calm might mentally associate it with a specific word like “Breathe” or a physical touch like touching their thumb and forefinger, pulling themselves back to that feeling when stressed. There is nothing so complex in the limitation that could not be doing in the human mind.
“mapping the digital records to feature vectors in a higher than three-dimensional vector space;” A humans are capable of observing data and mentally projecting the observed information into a k-dimensional vector space; for example, a person mentally maps a new restaurant as a point in a 4D space vector based on price, quality, distance and location. There is nothing so complex in the limitation that could not be doing in the human mind.
“calculating Hamming distances between the feature vectors;” A human is capable of observing two vectors and determining their difference through mental computation; for example, a person can mentally compute the difference A − B: A−B=(3−1,5−2)=(2,3) where the resulting vector difference is (2, 3). There is nothing so complex in the limitation that could not be doing in the human mind.
“forming labeled clusters of the feature vectors in the higher than three-dimensional vector space using:” A human is capable of mentally processing data from a k-dimensional vector and producing a label to designate the observed data; for example, a radiologist examines a 4D MRI scan and labels the region of interest as “malignant” or “benign” mentally. There is nothing so complex in the limitation that could not be doing in the human mind.
“autocorrelating the labeled clusters having common characteristics to identify transactions that recur on a predetermined time interval, thereby identifying time-recurrent labeled clusters;” A human mentally link a set of observed labels to ascertain similarities. Humans can also observe data and identify recurring patterns within a predefined interval. Humans are also capable of mentally observing data to identify and label which data points occur more or less frequently. For example, a commuter riding the train daily mentally notes that the 8:15 AM train is almost always packed (high frequency) while the 9:00 AM train is usually empty (low frequency), allowing them to label the 8:15 as the "busy" option. There is nothing so complex in the limitation that could not be doing in the human mind.
“identifying the anchor tags, wherein the anchor tags represent characteristics of groups of time-recurrent labeled clusters useful for resource management operations;” A can mentally observe multiple anchor tags and identify their characteristics; for example, an user can quickly scan a webpage and distinguish that the blue text in the header is an internal navigation link, while the underlined text at the bottom is an external link opening in a new tab. There is nothing so complex in the limitation that could not be doing in the human mind.
“applying the anchor tag to the group of time-recurrent labeled clusters based on the anchor tag selection signal, thereby creating an anchor tagged group of time-recurrent labeled clusters;” A human can mentally group multiple labels to form a single category; for example, A human can mentally group labels like “apple,” “banana,” and “orange” into the category “fruit,” where the human observes those terms occurring multiple times in human shopping lists. There is nothing so complex in the limitation that could not be doing in the human mind.
“generating a time-recurrent cluster monitoring signal based on an applied anchor tag;” A humans can mentally observe data and group them by applying criteria; for example, person sorts a deck of cards by color (red/black) and then by suit (hearts/diamonds/spades/clubs). There is nothing so complex in the limitation that could not be doing in the human mind.
As per dependent claims 2 and 12, the claims similarly recite the limitations of:
“identifying additional time-recurrent labeled clusters representing new digital records, over time, that have received the applied anchor tag and have been made a part of the anchor tagged group of time-recurrent labeled clusters;” A humans can observe a label and mentally determine if it belongs within a group based on the number of times they observe it occurring; for example, a individual might associate the label “healthy” with a specific food (e.g., broccoli) if they consistently observe themselves eating it before feeling energetic over several weeks. There is nothing so complex in the limitation that could not be doing in the human mind.
“monitoring the anchor tagged group of time-recurrent labeled clusters for movement over time;” A human can observe data as it continues to arrive; for example, a human observing data as it continues to arrive is a stock market trader monitoring a live trading dashboard. There is nothing so complex in the limitation that could not be doing in the human mind.
“on condition the anchor tagged group of time-recurrent labeled clusters moves beyond a predetermined threshold: initiating a management action to mitigate the movement.” A human trader can observe data on a live trading dashboard and mentally decide to buy or sell a stock; for example, after observing the 50-day moving average break upward on their dashboard, the day trader mentally decided to buy shares of the stock to capitalize on the new trend. There is nothing so complex in the limitation that could not be doing in the human mind.
As per dependent claims 3 and 13, the claims similarly recite the limitations of:
“wherein the management action to mitigate the movement includes at least one of forecasting and preparing for at least one of: a reallocation of resources into an account linked to the digital records; and the reallocation of resources out of the account linked to the digital records.” A human can observe data, make mental forecasts, and plan for the resulting outcomes; for example, a human can observe dark clouds, and mentally predicted rain and planned to bring an umbrella. There is nothing so complex in the limitation that could not be doing in the human mind.
As per dependent claims 4 and 14, the claims similarly recite the limitations of:
“wherein initiating the management action comprises releasing a gate to at least one of: initiate a reallocation of resources into an account linked to the digital records; and initiate the reallocation of resources out of the account linked to the digital records.” Per specification paragraph [0089] releasing a gate is nothing more than give a permission to initiate a monetary fund. A human can mentally decide to give permission to make a monetary payment or not; for example, after reviewing the final invoice for the car repair, I mentally decided the charge was fair and authorized the payment with my credit card. There is nothing so complex in the limitation that could not be doing in the human mind.
As per dependent claims 6 and 16, the claims similarly recite the limitations of:
“mapping the digital records comprises vectorizing the metadata, wherein the feature vectors are generated that are distributed numerical representations of the metadata.” A human can observe records and mentally map their information into a vector; for example, a real estate agent observes house records and mentally maps them into a vector of features—(price, square footage, bedrooms)—positioning a modern 3-bedroom home near similar listings in their mental "neighborhood" space. There is nothing so complex in the limitation that could not be doing in the human mind.
As per dependent claims 8 and 18, the claims similarly recite the limitations of:
“further comprising, prior to the logic to transform the digital records from the disparate computer server systems into visualizations and anchor tags, logic for: processing the digital records through a parser to generate a large sample set, wherein each sample in the large sample set comprises a sequence of one or more symbols;” A human can observe data and create samples from it; for example, researcher watching a busy coffee shop (observing data) selects every tenth customer to interview about their beverage preferences, creating a representative sample of consumer behavior. There is nothing so complex in the limitation that could not be doing in the human mind.
“high-pass filtering the large sample set to reduce its contents to the highest-frequency components, resulting in a filtered sample set;” A human can use criteria to further refine a sample set of data mentally; for example, while scanning a long list of restaurant reviews, a human can mentally filter out all one-star ratings to only consider highly-rated options for dinner. There is nothing so complex in the limitation that could not be doing in the human mind.
“utilizing the filtered sample set as the basis for mapping the digital records to the feature vectors in the higher-dimensional space.” "A human can observe filtered samples of data and mentally use them to create a k-dimensional space visualization for the sample set of data observed; for example, a detective reviews a few crime scene photos and instantly forms a mental 4D map of the room, estimating the positions of the suspect, victim, and weapon. There is nothing so complex in the limitation that could not be doing in the human mind.
As per dependent claims 10 and 20, the claims similarly recite the limitations of:
“identifying transaction dates from the feature vectors in the labeled clusters;” A human can mentally observe records and identify dates from them; for example, while scanning a medical chart, the nurse mentally noted "10/12/24" as the date of the last appointment. There is nothing so complex in the limitation that could not be doing in the human mind.
“applying a partial autocorrelation function (PACF) to the temporal series to determine transaction intervals, wherein the transaction intervals exhibit high correlation of the feature vectors of the temporal series;” A human can observe data and associate them based on the order in which they occur; for example, an observer notes a flash of lightning followed immediately by a roar of thunder, associating the two events as part of the same storm.
“comparing the transaction intervals to K, wherein K is a time interval representing a cadence useful for resource management operations;” A human can mentally observe data and identify intervals to define when that data occurs; for example, a retail manager observes daily sales data and identifies that the highest transaction volume occurs within the two-hour interval of 5:00 PM to 7:00 PM, allowing them to define this period as the peak "rush hour" for staffing purposes.
“on condition the transaction intervals fall within K: identifying the labeled clusters as the time-recurrent labeled clusters.” A human can observe groups of data and identify criteria that occur within an interval of time; for example, a teacher observing a student for 30 minutes, broken into 1-minute intervals, noting that "swearing," "hitting," or "on-task behavior" occurred at any point during specific 1-minute intervals.
Accordingly, claims 1-20 recite at least one abstract idea.
Step 2A, Prong II: Integrated into a Practical Application?
The claims recite the following additional limitations/elements:
As per independent claims 1 and 11, the claims also recite the additional element of “an interface; a plurality of disparate computer server systems; an ingest module; an outflow module; a logic; a higher than three-dimensional vector space; a density-based spatial clustering of applications with noise (DBSCAN) algorithm; Hamming distances; wherein the anchor tags represent characteristics of groups of time-recurrent labeled clusters useful for resource management operations; user interface logic; and initiating the resource management operations based on the time-recurrent cluster monitoring signal, thereby improving operational efficiencies of at least one of the ingest module and the outflow module by improving at least one of communication and operational bandwidth, system stability, and latency between the ingest module and the outflow module.” that is mere instructions to apply an exception. A recitation of the words "apply it" (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)).
The claims also recite the additional element of “receive digital records; receiving an anchor tag selection signal from the user comprising at least one of: selecting a suggested anchor tag; creating a custom anchor tag; and selecting no anchor tag;”, and the courts have recognized that receiving or transmitting data over a network, e.g., using the Internet to gather data, 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)(II)(i).
The claims also recite the additional elements of “presenting the visualizations and the anchor tags to a user for selection of the anchor tag;” that are the insignificant extra-solution activity of data gathering and/or output, and can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim (see MPEP 2106.05(g)).
As per dependent claims 2 and 12, the claims also recite the additional element of “on condition the anchor tagged group of time-recurrent labeled clusters moves beyond a predetermined threshold:” that is mere instructions to apply an exception. A recitation of the words "apply it" (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)).
As per dependent claims 3 and 13, the claims also recite the additional element of “a reallocation of resources into an account linked to the digital records; and the reallocation of resources out of the account linked to the digital records.” that is mere instructions to apply an exception. A recitation of the words "apply it" (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)).
As per dependent claims 4 and 14, the claims also recite the additional element of “initiate a reallocation of resources into an account linked to the digital records; and initiate the reallocation of resources out of the account linked to the digital records.” that is mere instructions to apply an exception. A recitation of the words "apply it" (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)).
As per dependent claims 5 and 15, the claims also recite the additional element of “wherein the resources are at least one of monetary funds and other digitally represented assets.” that is mere instructions to apply an exception. A recitation of the words "apply it" (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)).
As per dependent claims 6 and 16, the claims also recite the additional element of “wherein: the digital records include metadata comprising at least one of: text descriptions; resource amounts; source account information; transaction dates; and institution identifiers; wherein the feature vectors are generated that are distributed numerical representations of the metadata.” that is mere instructions to apply an exception. A recitation of the words "apply it" (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)).
As per dependent claims 7 and 17, the claims also recite the additional element of “wherein generating the feature vectors includes utilizing word2vec.” that is mere instructions to apply an exception. A recitation of the words "apply it" (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)).
As per dependent claims 8 and 18, the claims also recite the additional element of “a parser” that is mere instructions to apply an exception. A recitation of the words "apply it" (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)).
As per dependent claims 9 and 19, the claims also recite the additional element of “a minimum number of data points that make up a cluster; and a maximum distance between points in order to merge it into the cluster, wherein the maximum distance is determined at least in part from the Hamming distances; applying a labeling algorithm to the clusters of interest to generate labeled clusters of feature vectors in the higher than three-dimensional vector space, wherein the labeling algorithm comprises at least one of: Natural Language Processing algorithms; Natural Language Understanding algorithms; topical analysis algorithms; and subject analysis algorithms.” that is mere instructions to apply an exception. A recitation of the words "apply it" (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)).
The claims also recite the additional element of “providing the DBSCAN algorithm with: receiving clusters of interest after executing the DBSCAN algorithm; and passing the clusters of interest through a summary stage comprising:”, and the courts have recognized that receiving or transmitting data over a network, e.g., using the Internet to gather data, 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)(II)(i).
As per dependent claims 10 and 20, the claims also recite the additional element of “applying a partial autocorrelation function (PACF) to the temporal series; wherein K is a time interval representing a cadence useful for resource management operations;” that is mere instructions to apply an exception. A recitation of the words "apply it" (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)).
Therefore, claims 1-20 do not integrate the recited abstract ideas into a practical application.
Step 2B: Claim provides an Inventive Concept?
With respect to the limitations identified as insignificant extra-solution activity above the conclusions are carried over, and both the “receiving …; is well-understood, routine, and conventional operations.
For support as being well-understood, routine, and conventional for “receiving …; as noted by the courts is well understood routine and conventional, see MPEP 2106.05(d)(ii) “i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … 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);” and/or MPEP 2106.05(d)(ii) “iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;”, and/or MPEP 2106.05(d)(II) “iii. Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log);”.
Looking at the limitations in combination and the claim as a whole does not change this conclusion and the claim is ineligible.
Therefore, the claims 1-20 are not patent eligible.
Claim Rejections - 35 USC § 103
6. 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 of this title, 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 pre-AIA 35 U.S.C. § 103(a) 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.
7. Claims 1, 6-7, 11, 16-17 and 20 are rejected under 35 U.S.C. § 103 as being unpatentable over Chen et al. (US 20210110432 A1) in view of Healy (US 20150024945 A1) in further view of Stefik et al. (US 20180322172 A1).
As per claim 1, Chen teaches a system (i.e. “an item placement configuration system 104.”; fig. 1:104, para. [0036]) comprising:
an interface (i.e. “a user interface”; figs. 4-6, para. [0036]) coupled to receive digital records (i.e. “The item placement configuration system 104 represents functionality of the computing device 102 to receive a digital content item 106 to be output in a user interface.”; fig. 1, para. [0036]) from a plurality of disparate computer server systems (i.e. “a plurality of different domains”; fig. 1, para. [0025], [0036], [0039]; Examiner note: the plurality of disparate computer server systems is interpreted as the plurality of different domains),
wherein the interface includes an ingest module (i.e. “a rendering module 122.”; fig. 1:122, para. [0038]; Examiner note: the ingest module is interpreted as the rendering module);
an outflow module (i.e. “a similarity module 118”; fig. 1:118, para. [0038]; Examiner note: the outflow module is interpreted as the similarity module) comprising logic (i.e. “the similarity module 118 is configured to implement a t-distributed stochastic neighbor embedding (T-SNE) machine learning algorithm”; fig. 2, para. [0028], [0064]; Examiner note: the logic is interpreted as the t-distributed stochastic neighbor embedding (T-SNE) machine learning algorithm) to transform the digital records from the disparate computer server systems into visualizations (i.e. “a user of the item placement configuration system 104 may specify that the similarity module is to generate ten different entity clusters 206 using k-means clustering, and visualize the results of the k-means clustering using T-SNE to achieve the entity cluster 402 in the example implementation 400.”; fig. 4, para. [0064]-[0065], [0068] ) and anchor tags (i.e. “As illustrated in magnified detail 404, dot 406 may be representative of a first entity, dot 408 may be representative of a second entity, and dot 410 may be representative of a third entity.”; fig. 4, para. [0065]; Examiner note: the anchor tags are interpreted as the dots) by:
mapping the digital records to feature vectors in a higher than three-dimensional vector space (i.e. “the similarity module 118 obtains the k-dimensional vectors for the first and second entities from their respective entity representations 204.”; fig. 2, para. [0027]-[0028], [0062]-[0063], [0072]; Examiner notes: the feature vectors is interpreted as the k-dimensional vectors; K-dimensional can be considered as higher than three-dimensional vector space as it recited in para. [0063] it can be 64-dimension vector);
forming labeled clusters of the feature vectors in the higher than three-dimensional vector space using (i.e. “Using these vectors, the item placement configuration system generates one or more entity clusters. Each cluster provides a visual indication of the entity's overall similarity to other entities described in the multi-domain taxonomy. For instance, each entity may be visually represented as a dot in a cluster map, where entities having similar k-dimensional vector values are positioned close to one another, while dissimilar entities are positioned distant from one another.”; fig. 4, para. [0028], [0066]; Examiner note: the clusters or cluster map is generated in k-dimensional space, see fig. 4):
initiating the resource management operations based on the time-recurrent cluster monitoring signal (i.e. “The targeting controls 608 may further enable a user of the item placement configuration system 104 to specify certain audience segments for which the item 106 is to be presented, along with any other suitable control that enables a user of the item placement configuration system to specify placement criteria, such as date ranges, budget goals, optimization goals, and the like, for placement of the item 106.”; para. [0078]-[0079]; Examiner note: the initiating the resource management operations based on the time-recurrent cluster monitoring signal is interpreted as the enable a user of the item placement configuration system to specify certain audience segments for which the item is to be presented, along with any other suitable control that enables a user of the item placement configuration system to specify placement criteria, such as date ranges, budget goals, optimization goals, and the like),
thereby improving operational efficiencies of at least one of the ingest module and the outflow module by improving at least one of communication and operational bandwidth, system stability, and latency between the ingest module and the outflow module (i.e. “In this manner, the recommended placement for the digital content item can be generated and output online, or in real-time, without requiring the network and computational resources that are otherwise necessary to construct the multi-domain taxonomy on-demand (e.g., upon receipt of the request for the recommended placement). In this manner, the techniques described herein improve the efficiency of a computing device implementing the item placement configuration system while simultaneously reducing an amount of computational resources required by the computing device to do so.”; para. [0026]).
However, it is noted that the prior art of Chen does not explicitly teaches “calculating Hamming distances between the feature vectors; a density-based spatial clustering of applications with noise (DBSCAN) algorithm; and the Hamming distances; autocorrelating the labeled clusters having common characteristics to identify transactions that recur on a predetermined time interval, thereby identifying time-recurrent labeled clusters; and identifying the anchor tags, wherein the anchor tags represent characteristics of groups of time-recurrent labeled clusters useful for resource management operations; and user interface logic to apply the anchor tags to the time-recurrent labeled clusters and to facilitate resource management operations by: presenting the visualizations and the anchor tags to a user for selection of the anchor tag; receiving an anchor tag selection signal from the user comprising at least one of: selecting a suggested anchor tag; creating a custom anchor tag; and selecting no anchor tag; applying the anchor tag to the group of time-recurrent labeled clusters based on the anchor tag selection signal, thereby creating an anchor tagged group of time-recurrent labeled clusters; generating a time-recurrent cluster monitoring signal based on an applied anchor tag;”
On the other hand, in the same field of endeavor, Healy teaches calculating Hamming distances between the feature vectors (i.e. “a Hamming distance is calculated between the bits in each vector corresponding to the primer partitions observed in the current temporally segmented set.”; fig. 11, para. [0111], [0342]);
a density-based spatial clustering of applications with noise (DBSCAN) algorithm (i.e. “In that embodiment the DBSCAN algorithm was used to cluster dye intensity vectors.”; para. [0342]); and
the Hamming distances (i.e. “Column "1132" shows the Hamming distances between each of the reference vectors in "1130" versus the cohort vector in "1120".” and “The bit positions within both the cohort vector "1120" and all of the reference vectors in 1130 are masked prior to computing the Hamming Distances.”; fig. 11, para. [0111]);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Healy that teaches generating a plurality of target partitions into the prior art of Chen that teaches automatic item placement recommendation. Additionally, this can improve the efficiency of a computing device implementing the item placement configuration system while simultaneously reducing an amount of computational resources required by the computing device to do so.
The motivation for doing so would be to provide a method that can calculate vector distances, as this can facilitate the final assay readout, indicating the presence or absence of a particular target of interest in the sample (Healy, para. [0111], [0158]).
However, it is noted that the combination of the prior arts of Chen and Healy do not explicitly teach “autocorrelating the labeled clusters having common characteristics to identify transactions that recur on a predetermined time interval, thereby identifying time-recurrent labeled clusters; and identifying the anchor tags, wherein the anchor tags represent characteristics of groups of time-recurrent labeled clusters useful for resource management operations; and user interface logic to apply the anchor tags to the time-recurrent labeled clusters and to facilitate resource management operations by: presenting the visualizations and the anchor tags to a user for selection of the anchor tag; receiving an anchor tag selection signal from the user comprising at least one of: selecting a suggested anchor tag; creating a custom anchor tag; and selecting no anchor tag; applying the anchor tag to the group of time-recurrent labeled clusters based on the anchor tag selection signal, thereby creating an anchor tagged group of time-recurrent labeled clusters; generating a time-recurrent cluster monitoring signal based on an applied anchor tag;”
On the other hand, in the same field of endeavor, Stefik teaches autocorrelating the labeled clusters having common characteristics (i.e. “Besides using the filters for search specific events, similar events can be searched and obtained based on the similarity of the parameter values for each event without using a specific set of search filters.” and “In a further embodiment, the similarity slider can include or exclude events according to their aggregate degree of difference in parameter values.”; fig. 21, para. [0084]; Examiner note: the having common characteristics is interpreted as the similar events or event that share similarities; the autocorrelating can be interpreted as the similarity slider can include or exclude events according to their aggregate degree of difference in parameter values. Further, i.e. “In some variations of trend analysis, clustering of event objects may be performed according to their parameter values.”; para. [0097]) to identify transactions (i.e. “Tracking metrics in the analytics can identify some trends among event objects.”; para. [0098]; Examiner note: the identify transactions is interpreted as the identify some trends among event objects) that recur on a predetermined time interval (i.e. “Examples of the time period qualifier can be last six months, between 10:00 am and 12:00 am, on weekend evenings, in the last week of the month, and so on.”; fig. 25, para. [0098]),
thereby identifying time-recurrent labeled clusters (i.e. “A recurring event can be easily and efficiently planned by using the analytics data from an earlier time on an event planning and analytics dashboard. By way of example, FIG. 28 is a screenshot showing the event planning and analytics dashboard 360 displaying analytics data for planning a recurring event. In this example, the analytics data of the Happy Parade occurred in 2013 is used as a base for planning to duplicate the analytics data on the event planning and analytics dashboard.”; fig. 28, para. [0089], [0091]; Examiner note: the time-recurrent labeled clusters is interpreted as the analytics data for planning a recurring event, see fig. 28:360); and
identifying the anchor tags (i.e. “FIG. 6 is a flow diagram showing a routine 90 for associating the activity data with tags for use in the method of FIG. 2. Once the activity data is created (step 91), a tag can be specified. Tags can indicate contexts and purposes of the activity data. A creator of the activity data can select tags from a collection of predefined tags (step 92). One or more tags can be selected for a single activity data.”; figs. 1B, 6, para. [0067]; Examiner note: the identifying the anchor tags is interpreted as the one or more tags can be selected for a single activity data),
wherein the anchor tags represent characteristics of groups of time-recurrent labeled clusters useful for resource management operations (i.e. “The predefined tags can include, for example, “#department store,” “#kitchen fire,” “#out-of-business hours,” and so on.” and “Tagging can be done at the time when the event object is created or after the event object is created. Referring back to FIG. 5, Dispatcher Anderson adds a tag 85 “#new_event” tag to his activity data 84.”; figs. 5-6, para. [0067]-[0068]; Examiner note: the anchor tags represent characteristics of groups of time-recurrent labeled clusters useful for resource management operations is interpreted as the predefined tags can include, for example, “#department store,” “#kitchen fire,” “#out-of-business hours,” and so on. Further, i.e. “The organization operating system can be a standard, enterprise-wide collection of business processes and can run complex business programs, such as workflow management systems, payroll management systems, human resources management systems”; para. [0056]); and
user interface logic (i.e. “The workflow data 18 can be provided to computers 12-15 of the mobile workers through a Web interface 21, 22 or a graphical user interface 23, 24, such as a dashboard application, as well as other types of user interfaces.”; para. [0048], [0054], [0058], [0066])
to apply the anchor tags to the time-recurrent labeled clusters (i.e. “when the worker creates activity data via the user interface, one or more tags can be associated with the activity data” and “the logs can be chronologically organized based on the time that the activity data is tagged with event tags”; fig. 11, para. [0060], [0066]-[0067], [0073]) and
to facilitate resource management operations by (i.e. “A staffing chart 109 can display how much resources are used for the event object by indicating how many workers are currently assigned to the event on real-time and the time axis of the graph can track the transition of staff number variation as the time goes.”; para. [0069], [0078]):
presenting the visualizations and the anchor tags to a user for selection of the anchor tag (i.e. “the tag module 32 can provide predefined tags 31 for the workers to select the best matched tag to describe the activity data, including types, purposes, and nature or context of the activities.”; para. [0053]);
receiving an anchor tag selection signal from the user (i.e. “the tag module 32 can provide predefined tags 31 for the workers to select the best matched tag to describe the activity data, including types, purposes, and nature or context of the activities…. All the automatic tags, predefined tags, and manually entered tags are maintained in the database 19.”; para. [0053]) comprising at least one of:
selecting a suggested anchor tag; creating a custom anchor tag; and selecting no anchor tag (i.e. “predefined tags 31 for the workers to select the best matched tag”; para. [0053]. Further, i.e. “Equipment Inventory of In this example, the In variations, the messages could equipment equipment used is have explicit parameters to used. determined from the log distinguish and report on messages tagged with different types of equipment. “#add_equipment.””; para. [0079]);
applying the anchor tag to the group of time-recurrent labeled clusters based on the anchor tag selection signal (i.e. “activity data 27 can be tagged with tags 31”; para. [0052]; Examiner note: the group of time-recurrent labeled clusters is interpreted as the activity data 27),
thereby creating an anchor tagged group of time-recurrent labeled clusters (i.e. “The search results 402 can be displayed as aggregate of the events in each class, such as “fire,” “parade,” “storm,” and “sports.””; para. [0096]; Examiner note: the creating the anchor tagged group of time-recurrent labeled clusters is interpreted as the aggregate of the events in each class. Further, i.e. “Such automatic tags can include author's identity, location, and time. Other types of automatic tags are possible.”; para. [0054], [0067]; Examiner note: where the automatic tags are created);
generating a time-recurrent cluster monitoring signal based on an applied anchor tag (i.e. “Once the analytics are performed, the analytics data can be used for further analysis, such as reviewing and evaluating organizational performance (step 50), planning recurring events (step 51), and performing trend analysis (step 52).”; para. [0062]. Further, i.e. “the analytics data can include parameter values corresponding to event classes of the event objects.”; para. [0092]; Examiner note: the monitoring signal is interpreted as the analytics data);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Stefik that teaches generating recurring events into the combination of the prior arts of Chen that teaches automatic item placement recommendation, and Healy that teaches generating a plurality of target partitions. Additionally, this can improve the efficiency of a computing device implementing the item placement configuration system while simultaneously reducing an amount of computational resources required by the computing device to do so.
The motivation for doing so would be to provide a management tool that can quickly and efficiently organize and monitor tasks, enabling more effective oversight and decision-making (Stefik, para. [0004], [0092]).
As per claim 6, Chen, Healy and Stefik teach all the limitations as discussed in claim 1 above.
Additionally, Chen teaches wherein: the digital records include metadata comprising at least one of: text descriptions; resource amounts; source account information; transaction dates; and institution identifiers (i.e. “items that are associated with individual entities and collect information associated with the items, such as associated textual descriptions, embedded metadata, and so forth.”; para. [0039]); and
mapping the digital records comprises vectorizing the metadata (i.e. “the item placement configuration system may crawl various domains to identify items and collect information associated with the items, such as associated textual descriptions, embedded metadata...information describing the various items associated with the entity is compiled into a k-dimensional vector,”; para. [0006], [0028]),
wherein the feature vectors are generated that are distributed numerical representations of the metadata (i.e. “the value of k may be any suitable number and is limited only by a number of different leaf nodes of the multi-domain taxonomy 202, such as a number of different item classifications included in the tier-3 category column 306 of the example implementation 300.”; para. [0063], [0067], [0071]-[0073]).
As per claim 7, Chen, Healy and Stefik teach all the limitations as discussed in claim 6 above.
Additionally, Chen teaches wherein generating the feature vectors includes utilizing word2vec (i.e. “Word2Vec”; fig. 3, para. [0085]).
As per claim 10, Chen, Healy and Stefik teach all the limitations as discussed in claim 1 above.
However, it is noted that the combination of the prior arts of Chen and Healy do not explicitly teach “wherein autocorrelating the labeled clusters having common characteristics includes: identifying transaction dates from the feature vectors in the labeled clusters; forming a temporal series based at least in part on the transaction dates; applying a partial autocorrelation function (PACF) to the temporal series to determine transaction intervals, wherein the transaction intervals exhibit high correlation of the feature vectors of the temporal series; comparing the transaction intervals to K, wherein K is a time interval representing a cadence useful for resource management operations; and on condition the transaction intervals fall within K: identifying the labeled clusters as the time-recurrent labeled clusters.”
On the other hand, in the same field of endeavor, Stefik teaches wherein autocorrelating the labeled clusters having common characteristics includes: identifying transaction dates from the feature vectors in the labeled clusters (i.e. “Referring back to FIG. 15, the fire events for last ten years are displayed on the search dashboard sorted by date.”; para. [0079]; Examiner note: The identifying transaction dates is interpreted as the fire events for last ten years are displayed on the search dashboard sorted by date);
forming a temporal series based at least in part on the transaction dates (i.e. “Examples of temporal filters for trend analysis: Intervals in a day: mornings, afternoons, evenings, and late night. Intervals from daily human rhythms: working hours, school hours, rush or heavy commuting hours, day light hours, and train and bus arrival times.”; para. [0100]; the temporal series are interpreted as the Intervals in a day, Intervals from daily human rhythms);
applying a partial autocorrelation function (PACF) to the temporal series to determine transaction intervals, wherein the transaction intervals exhibit high correlation of the feature vectors of the temporal series (i.e. “if a reviewer wants to narrow the search only to high parameter values, the parameter value as a filter can be set high.”; para. [0084]);
comparing the transaction intervals to K, wherein K is a time interval representing a cadence (i.e. “a reviewer desires to see a certain trend in a specified time period when the data in the specified period is compared with data in other time frames.”; para. [0099]; examiner note: the cadence is interpreted as the trend) useful for resource management operations (i.e. “The organization operating system can be a standard, enterprise-wide collection of business processes and can run complex business programs, such as workflow management systems, payroll management systems, human resources management systems,”; para. [0056]); and
on condition the transaction intervals fall within K (i.e. “For the event objects in the preferred time period, temporal filters are also applied to narrow and discover event objects limited in the certain period of time (step 416)”; para. [0100]):
identifying the labeled clusters as the time-recurrent labeled clusters (i.e. “all the fire events in the list are sorted by the length of the duration and the bands 292 divide the events as high 293, normal 294, and low 295”; para. [0084]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Stefik that teaches generating recurring events into the combination of the prior arts of Chen that teaches automatic item placement recommendation, and Healy that teaches generating a plurality of target partitions. Additionally, this can improve the efficiency of a computing device implementing the item placement configuration system while simultaneously reducing an amount of computational resources required by the computing device to do so.
The motivation for doing so would be to provide a management tool that can quickly and efficiently organize and monitor tasks, enabling more effective oversight and decision-making (Stefik, para. [0004], [0092]).
As per claim 11, Chen teaches a method (i.e. “a method”; para. [0108]) comprising:
receiving, via an interface (i.e. “a user interface”; figs. 4-6, para. [0036]), digital records (i.e. “The item placement configuration system 104 represents functionality of the computing device 102 to receive a digital content item 106 to be output in a user interface.”; fig. 1, para. [0036]) from a plurality of disparate computer server systems (i.e. “a plurality of different domains”; fig. 1, para. [0025], [0036], [0039]; Examiner note: the plurality of disparate computer server systems is interpreted as the plurality of different domains),
wherein the interface includes an ingest module (i.e. “a rendering module 122.”; fig. 1:122, para. [0038]; Examiner note: the ingest module is interpreted as the rendering module);
transforming, using an outflow module (i.e. “a similarity module 118”; fig. 1:118, para. [0038]; Examiner note: the outflow module is interpreted as the similarity module), the digital records from the disparate computer server systems into visualizations (i.e. “a user of the item placement configuration system 104 may specify that the similarity module is to generate ten different entity clusters 206 using k-means clustering, and visualize the results of the k-means clustering using T-SNE to achieve the entity cluster 402 in the example implementation 400.”; fig. 4, para. [0064]-[0065], [0068] ) and anchor tags (i.e. “As illustrated in magnified detail 404, dot 406 may be representative of a first entity, dot 408 may be representative of a second entity, and dot 410 may be representative of a third entity.”; fig. 4, para. [0065]; Examiner note: the anchor tags are interpreted as the dots) by:
mapping the digital records to feature vectors in a higher than three-dimensional vector space (i.e. “the similarity module 118 obtains the k-dimensional vectors for the first and second entities from their respective entity representations 204.”; fig. 2, para. [0027]-[0028], [0062]-[0063], [0072]; Examiner notes: the feature vectors is interpreted as the k-dimensional vectors; K-dimensional can be considered as higher than three-dimensional vector space as it recited in para. [0063] it can be 64-dimension vector);
forming labeled clusters of the feature vectors in the higher than three-dimensional vector space using (i.e. “Using these vectors, the item placement configuration system generates one or more entity clusters. Each cluster provides a visual indication of the entity's overall similarity to other entities described in the multi-domain taxonomy. For instance, each entity may be visually represented as a dot in a cluster map, where entities having similar k-dimensional vector values are positioned close to one another, while dissimilar entities are positioned distant from one another.”; fig. 4, para. [0028], [0066]; Examiner note: the clusters or cluster map is generated in k-dimensional space, see fig. 4):
initiating the resource management operations based on the time-recurrent cluster monitoring signal (i.e. “The targeting controls 608 may further enable a user of the item placement configuration system 104 to specify certain audience segments for which the item 106 is to be presented, along with any other suitable control that enables a user of the item placement configuration system to specify placement criteria, such as date ranges, budget goals, optimization goals, and the like, for placement of the item 106.”; para. [0078]-[0079]; Examiner note: the initiating the resource management operations based on the time-recurrent cluster monitoring signal is interpreted as the enable a user of the item placement configuration system to specify certain audience segments for which the item is to be presented, along with any other suitable control that enables a user of the item placement configuration system to specify placement criteria, such as date ranges, budget goals, optimization goals, and the like),
thereby improving operational efficiencies of at least one of the ingest module and the outflow module by improving at least one of communication and operational bandwidth, system stability, and latency between the ingest module and the outflow module (i.e. “In this manner, the recommended placement for the digital content item can be generated and output online, or in real-time, without requiring the network and computational resources that are otherwise necessary to construct the multi-domain taxonomy on-demand (e.g., upon receipt of the request for the recommended placement). In this manner, the techniques described herein improve the efficiency of a computing device implementing the item placement configuration system while simultaneously reducing an amount of computational resources required by the computing device to do so.”; para. [0026]).
However, it is noted that the prior art of Chen does not explicitly teaches “calculating Hamming distances between the feature vectors; a density-based spatial clustering of applications with noise (DBSCAN) algorithm; and the Hamming distances; autocorrelating the labeled clusters having common characteristics to identify transactions that recur on a predetermined time interval, thereby identifying time-recurrent labeled clusters; and identifying the anchor tags, wherein the anchor tags represent characteristics of groups of time-recurrent labeled clusters useful for resource management operations; and applying, using user interface logic, the anchor tags to the time-recurrent labeled clusters and facilitating resource management operations by: presenting the visualizations and the anchor tags to a user for selection of the anchor tag; receiving an anchor tag selection signal from the user comprising at least one of: selecting a suggested anchor tag; creating a custom anchor tag; and selecting no anchor tag; applying the anchor tag to the group of time-recurrent labeled clusters based on the anchor tag selection signal, thereby creating an anchor tagged group of time-recurrent labeled clusters; generating a time-recurrent cluster monitoring signal based on an applied anchor tag;”
On the other hand, in the same field of endeavor, Healy teaches calculating Hamming distances between the feature vectors (i.e. “a Hamming distance is calculated between the bits in each vector corresponding to the primer partitions observed in the current temporally segmented set.”; fig. 11, para. [0111], [0342]);
a density-based spatial clustering of applications with noise (DBSCAN) algorithm (i.e. “In that embodiment the DBSCAN algorithm was used to cluster dye intensity vectors.”; para. [0342]); and
the Hamming distances (i.e. “Column "1132" shows the Hamming distances between each of the reference vectors in "1130" versus the cohort vector in "1120".” and “The bit positions within both the cohort vector "1120" and all of the reference vectors in 1130 are masked prior to computing the Hamming Distances.”; fig. 11, para. [0111]);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Healy that teaches generating a plurality of target partitions into the prior art of Chen that teaches automatic item placement recommendation. Additionally, this can improve the efficiency of a computing device implementing the item placement configuration system while simultaneously reducing an amount of computational resources required by the computing device to do so.
The motivation for doing so would be to provide a method that can calculate vector distances, as this can facilitate the final assay readout, indicating the presence or absence of a particular target of interest in the sample (Healy, para. [0111], [0158]).
However, it is noted that the combination of the prior arts of Chen and Healy do not explicitly teach “autocorrelating the labeled clusters having common characteristics to identify transactions that recur on a predetermined time interval, thereby identifying time-recurrent labeled clusters; and identifying the anchor tags, wherein the anchor tags represent characteristics of groups of time-recurrent labeled clusters useful for resource management operations; and applying, using user interface logic, the anchor tags to the time-recurrent labeled clusters and facilitating resource management operations by: presenting the visualizations and the anchor tags to a user for selection of the anchor tag; receiving an anchor tag selection signal from the user comprising at least one of: selecting a suggested anchor tag; creating a custom anchor tag; and selecting no anchor tag; applying the anchor tag to the group of time-recurrent labeled clusters based on the anchor tag selection signal, thereby creating an anchor tagged group of time-recurrent labeled clusters; generating a time-recurrent cluster monitoring signal based on an applied anchor tag;”
On the other hand, in the same field of endeavor, Stefik teaches autocorrelating the labeled clusters having common characteristics (i.e. “Besides using the filters for search specific events, similar events can be searched and obtained based on the similarity of the parameter values for each event without using a specific set of search filters.” and “In a further embodiment, the similarity slider can include or exclude events according to their aggregate degree of difference in parameter values.”; fig. 21, para. [0084]; Examiner note: the having common characteristics is interpreted as the similar events or event that share similarities; the autocorrelating can be interpreted as the similarity slider can include or exclude events according to their aggregate degree of difference in parameter values. Further, i.e. “In some variations of trend analysis, clustering of event objects may be performed according to their parameter values.”; para. [0097]) to identify transactions (i.e. “Tracking metrics in the analytics can identify some trends among event objects.”; para. [0098]; Examiner note: the identify transactions is interpreted as the identify some trends among event objects) that recur on a predetermined time interval (i.e. “Examples of the time period qualifier can be last six months, between 10:00 am and 12:00 am, on weekend evenings, in the last week of the month, and so on.”; fig. 25, para. [0098]),
thereby identifying time-recurrent labeled clusters (i.e. “A recurring event can be easily and efficiently planned by using the analytics data from an earlier time on an event planning and analytics dashboard. By way of example, FIG. 28 is a screenshot showing the event planning and analytics dashboard 360 displaying analytics data for planning a recurring event. In this example, the analytics data of the Happy Parade occurred in 2013 is used as a base for planning to duplicate the analytics data on the event planning and analytics dashboard.”; fig. 28, para. [0089], [0091]; Examiner note: the time-recurrent labeled clusters are interpreted as the analytics data for planning a recurring event, see fig. 28:360); and
identifying the anchor tags (i.e. “FIG. 6 is a flow diagram showing a routine 90 for associating the activity data with tags for use in the method of FIG. 2. Once the activity data is created (step 91), a tag can be specified. Tags can indicate contexts and purposes of the activity data. A creator of the activity data can select tags from a collection of predefined tags (step 92). One or more tags can be selected for a single activity data.”; figs. 1B, 6, para. [0067]; Examiner note: the identifying the anchor tags is interpreted as the one or more tags can be selected for a single activity data),
wherein the anchor tags represent characteristics of groups of time-recurrent labeled clusters useful for resource management operations (i.e. “The predefined tags can include, for example, “#department store,” “#kitchen fire,” “#out-of-business hours,” and so on.” and “Tagging can be done at the time when the event object is created or after the event object is created. Referring back to FIG. 5, Dispatcher Anderson adds a tag 85 “#new_event” tag to his activity data 84.”; figs. 5-6, para. [0067]-[0068]; Examiner note: the anchor tags represent characteristics of groups of time-recurrent labeled clusters useful for resource management operations is interpreted as the predefined tags can include, for example, “#department store,” “#kitchen fire,” “#out-of-business hours,” and so on. Further, i.e. “The organization operating system can be a standard, enterprise-wide collection of business processes and can run complex business programs, such as workflow management systems, payroll management systems, human resources management systems”; para. [0056]); and
applying, using user interface logic (i.e. “The workflow data 18 can be provided to computers 12-15 of the mobile workers through a Web interface 21, 22 or a graphical user interface 23, 24, such as a dashboard application, as well as other types of user interfaces.”; para. [0048], [0054], [0058], [0066]), the anchor tags to the time-recurrent labeled clusters (i.e. “when the worker creates activity data via the user interface, one or more tags can be associated with the activity data” and “the logs can be chronologically organized based on the time that the activity data is tagged with event tags”; fig. 11, para. [0060], [0066]-[0067], [0073]) and facilitating resource management operations (i.e. “A staffing chart 109 can display how much resources are used for the event object by indicating how many workers are currently assigned to the event on real-time and the time axis of the graph can track the transition of staff number variation as the time goes.”; para. [0069], [0078]) by:
presenting the visualizations and the anchor tags to a user for selection of the anchor tag (i.e. “the tag module 32 can provide predefined tags 31 for the workers to select the best matched tag to describe the activity data, including types, purposes, and nature or context of the activities.”; para. [0053]);
receiving an anchor tag selection signal from the user (i.e. “the tag module 32 can provide predefined tags 31 for the workers to select the best matched tag to describe the activity data, including types, purposes, and nature or context of the activities….All the automatic tags, predefined tags, and manually entered tags are maintained in the database 19.”; para. [0053]) comprising at least one of: selecting a suggested anchor tag; creating a custom anchor tag; and selecting no anchor tag (i.e. “predefined tags 31 for the workers to select the best matched tag”; para. [0053]. Further, i.e. “Equipment Inventory of In this example, the In variations, the messages could equipment equipment used is have explicit parameters to used. determined from the log distinguish and report on messages tagged with different types of equipment. “#add_equipment.””; para. [0079]);
applying the anchor tag to the group of time-recurrent labeled clusters based on the anchor tag selection signal (i.e. “activity data 27 can be tagged with tags 31”; para. [0052]; Examiner note: the group of time-recurrent labeled clusters is interpreted as the activity data 27),
thereby creating an anchor tagged group of time-recurrent labeled clusters (i.e. “The search results 402 can be displayed as aggregate of the events in each class, such as “fire,” “parade,” “storm,” and “sports.””; para. [0096]; Examiner note: the creating the anchor tagged group of time-recurrent labeled clusters is interpreted as the aggregate of the events in each class. Further, i.e. “Such automatic tags can include author's identity, location, and time. Other types of automatic tags are possible.”; para. [0054], [0067]; Examiner note: where the automatic tags are created);
generating a time-recurrent cluster monitoring signal based on an applied anchor tag (i.e. “Once the analytics are performed, the analytics data can be used for further analysis, such as reviewing and evaluating organizational performance (step 50), planning recurring events (step 51), and performing trend analysis (step 52).”; para. [0062]. Further, i.e. “the analytics data can include parameter values corresponding to event classes of the event objects.”; para. [0092]; Examiner note: the monitoring signal is interpreted as the analytics data);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Stefik that teaches generating recurring events into the combination of the prior arts of Chen that teaches automatic item placement recommendation, and Healy that teaches generating a plurality of target partitions. Additionally, this can improve the efficiency of a computing device implementing the item placement configuration system while simultaneously reducing an amount of computational resources required by the computing device to do so.
The motivation for doing so would be to provide a management tool that can quickly and efficiently organize and monitor tasks, enabling more effective oversight and decision-making (Stefik, para. [0004], [0092]).
As per claim 16, Chen, Healy and Stefik teach all the limitations as discussed in claim 11 above.
Additionally, Chen teaches wherein: the digital records include metadata comprising at least one of: text descriptions; resource amounts; source account information; transaction dates; and institution identifiers (i.e. “items that are associated with individual entities and collect information associated with the items, such as associated textual descriptions, embedded metadata, and so forth.”; para. [0039]); and
mapping the digital records comprises vectorizing the metadata (i.e. “the item placement configuration system may crawl various domains to identify items and collect information associated with the items, such as associated textual descriptions, embedded metadata...information describing the various items associated with the entity is compiled into a k-dimensional vector,”; para. [0006], [0028]),
wherein the feature vectors are generated that are distributed numerical representations of the metadata (i.e. “the value of k may be any suitable number and is limited only by a number of different leaf nodes of the multi-domain taxonomy 202, such as a number of different item classifications included in the tier-3 category column 306 of the example implementation 300.”; para. [0063], [0067], [0071]-[0073]).
As per claim 17, Chen, Healy and Stefik teach all the limitations as discussed in claim 16 above.
Additionally, Chen teaches wherein generating the feature vectors includes utilizing word2vec (i.e. “Word2Vec”; fig. 3, para. [0085]).
As per claim 20, Chen, Healy and Stefik teach all the limitations as discussed in claim 11 above.
However, it is noted that the combination of the prior arts of Chen and Healy do not explicitly teach “ wherein autocorrelating the labeled clusters having common characteristics includes: identifying transaction dates from the feature vectors in the labeled clusters; forming a temporal series based at least in part on the transaction dates; applying a partial autocorrelation function (PACF) to the temporal series to determine transaction intervals, wherein the transaction intervals exhibit high correlation of the feature vectors of the temporal series; comparing the transaction intervals to K, wherein K is a time interval representing a cadence useful for resource management operations; and on condition the transaction intervals fall within K: identifying the labeled clusters as the time-recurrent labeled clusters.”
On the other hand, in the same field of endeavor, Stefik teaches wherein autocorrelating the labeled clusters having common characteristics includes: identifying transaction dates from the feature vectors in the labeled clusters (i.e. “Referring back to FIG. 15, the fire events for last ten years are displayed on the search dashboard sorted by date.”; para. [0079]; Examiner note: The identifying transaction dates is interpreted as the fire events for last ten years are displayed on the search dashboard sorted by date);
forming a temporal series based at least in part on the transaction dates (i.e. “Examples of temporal filters for trend analysis: Intervals in a day: mornings, afternoons, evenings, and late night. Intervals from daily human rhythms: working hours, school hours, rush or heavy commuting hours, day light hours, and train and bus arrival times.”; para. [0100]; the temporal series are interpreted as the Intervals in a day, Intervals from daily human rhythms);
applying a partial autocorrelation function (PACF) to the temporal series to determine transaction intervals, wherein the transaction intervals exhibit high correlation of the feature vectors of the temporal series (i.e. “if a reviewer wants to narrow the search only to high parameter values, the parameter value as a filter can be set high.”; para. [0084]);
comparing the transaction intervals to K, wherein K is a time interval representing a cadence (i.e. “a reviewer desires to see a certain trend in a specified time period when the data in the specified period is compared with data in other time frames.”; para. [0099]; examiner note: the cadence is interpreted as the trend) useful for resource management operations (i.e. “The organization operating system can be a standard, enterprise-wide collection of business processes and can run complex business programs, such as workflow management systems, payroll management systems, human resources management systems,”; para. [0056]); and
on condition the transaction intervals fall within K (i.e. “For the event objects in the preferred time period, temporal filters are also applied to narrow and discover event objects limited in the certain period of time (step 416)”; para. [0100]):
identifying the labeled clusters as the time-recurrent labeled clusters (i.e. “all the fire events in the list are sorted by the length of the duration and the bands 292 divide the events as high 293, normal 294, and low 295”; para. [0084]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Stefik that teaches generating recurring events into the combination of the prior arts of Chen that teaches automatic item placement recommendation, and Healy that teaches generating a plurality of target partitions. Additionally, this can improve the efficiency of a computing device implementing the item placement configuration system while simultaneously reducing an amount of computational resources required by the computing device to do so.
The motivation for doing so would be to provide a management tool that can quickly and efficiently organize and monitor tasks, enabling more effective oversight and decision-making (Stefik, para. [0004], [0092]).
8. Claims 2 and 12 are rejected under 35 U.S.C. § 103 as being unpatentable over Chen et al. (US 20210110432 A1) in view of Healy (US 20150024945 A1) in further view of Stefik et al. (US 20180322172 A1) still in further view of Schimmelpfeng et al. (US 20180011771 A1).
As per claim 2, Chen, Healy and Stefik teach all the limitations as discussed in claim 1 above.
Additionally, Chen teaches further comprising: identifying additional time-recurrent labeled clusters representing new digital records, over time, that have received the applied anchor tag and have been made a part of the anchor tagged group of time-recurrent labeled clusters (i.e. entity to publish given item digitally and creating clusters and associating a word or topic with cluster and determine similarity with previous clusters where this process as an on going process with previous entities reads on over time and additional clusters; para. [0023], [0027]-[0029]);
However, it is noted that the combination of the prior arts of Chen, Healy and Stefik do not explicitly teach “wherein the resource management operations include: monitoring the anchor tagged group of time-recurrent labeled clusters for movement over time; and on condition the anchor tagged group of time-recurrent labeled clusters moves beyond a predetermined threshold: initiating a management action to mitigate the movement.”
On the other hand, in the same field of endeavor, Schimmelpfeng teaches wherein the resource management operations include: monitoring the anchor tagged group of time-recurrent labeled clusters for movement over time (i.e. “systems and methods for monitoring changes to an application.”; para. [0010]. Further, i.e. “If the number of changes or a metric associated with the number of changes added to the cluster is within the cluster range (e.g., exceeds a cluster threshold, exceeds a cluster threshold within a certain time period (e.g., within 1 day, week, month, year, etc.), etc.), an update may be made to the application that correlates to the changes within the cluster.”; para. [0019]); and
on condition the anchor tagged group of time-recurrent labeled clusters moves beyond a predetermined threshold (i.e. “For example, if two changes are evaluated and have a change metric within a given range (e.g. a “change range” or “change threshold”), they may be grouped into the same duster.”; para. [0018]. Further, i.e. “If the number of changes or a metric associated with the number of changes added to the cluster is within the cluster range (e.g., exceeds a cluster threshold, exceeds a cluster threshold within a certain time period (e.g., within 1 day, week, month, year, etc.), etc.), an update may be made to the application that correlates to the changes within the cluster.”; para. [0019]):
initiating a management action to mitigate the movement (i.e. if a number of changes area added to a cluster exceed a threshold within a certain period of time then an update correlating to the changes of the cluster are made where the update reads on mitigate; para. [0019]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Schimmelpfeng that teaches monitoring changes to an application into the combination of the prior arts of Chen that teaches automatic item placement recommendation, Healy that teaches generating a plurality of target partitions, and Stefik that teaches generating recurring events. Additionally, this can improve the efficiency of a computing device implementing the item placement configuration system while simultaneously reducing an amount of computational resources required by the computing device to do so.
The motivation for doing so would be to monitor changes systematically and provide the resulting data, ensuring that adjustments and decisions can be made effectively (Schimmelpfeng, para. [0018]-[0019]).
As per claim 12, Chen, Healy and Stefik teach all the limitations as discussed in claim 11 above.
Additionally, Chen teaches further comprising: identifying additional time-recurrent labeled clusters representing new digital records, over time, that have received the applied anchor tag and have been made a part of the anchor tagged group of time-recurrent labeled clusters (i.e. entity to publish given item digitally and creating clusters and associating a word or topic with cluster and determine similarity with previous clusters where this process as an on going process with previous entities reads on over time and additional clusters; para. [0023], [0027]-[0029]);
However, it is noted that the combination of the prior arts of Chen, Healy and Stefik do not explicitly teach “wherein the resource management operations include: monitoring the anchor tagged group of time-recurrent labeled clusters for movement over time; and on condition the anchor tagged group of time-recurrent labeled clusters moves beyond a predetermined threshold: initiating a management action to mitigate the movement.”
On the other hand, in the same field of endeavor, Schimmelpfeng teaches wherein the resource management operations include: monitoring the anchor tagged group of time-recurrent labeled clusters for movement over time (i.e. “systems and methods for monitoring changes to an application.”; para. [0010]. Further, i.e. “If the number of changes or a metric associated with the number of changes added to the cluster is within the cluster range (e.g., exceeds a cluster threshold, exceeds a cluster threshold within a certain time period (e.g., within 1 day, week, month, year, etc.), etc.), an update may be made to the application that correlates to the changes within the cluster.”; para. [0019]); and
on condition the anchor tagged group of time-recurrent labeled clusters moves beyond a predetermined threshold (i.e. “For example, if two changes are evaluated and have a change metric within a given range (e.g. a “change range” or “change threshold”), they may be grouped into the same duster.”; para. [0018]. Further, i.e. “If the number of changes or a metric associated with the number of changes added to the cluster is within the cluster range (e.g., exceeds a cluster threshold, exceeds a cluster threshold within a certain time period (e.g., within 1 day, week, month, year, etc.), etc.), an update may be made to the application that correlates to the changes within the cluster.”; para. [0019]):
initiating a management action to mitigate the movement (i.e. if a number of changes area added to a cluster exceed a threshold within a certain period of time then an update correlating to the changes of the cluster are made where the update reads on mitigate; para. [0019]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Schimmelpfeng that teaches monitoring changes to an application into the combination of the prior arts of Chen that teaches automatic item placement recommendation, Healy that teaches generating a plurality of target partitions, and Stefik that teaches generating recurring events. Additionally, this can improve the efficiency of a computing device implementing the item placement configuration system while simultaneously reducing an amount of computational resources required by the computing device to do so.
The motivation for doing so would be to monitor changes systematically and provide the resulting data, ensuring that adjustments and decisions can be made effectively (Schimmelpfeng, para. [0018]-[0019]).
9. Claims 3-5 and 13-15 are rejected under 35 U.S.C. § 103 as being unpatentable over Chen et al. (US 20210110432 A1) in view of Healy (US 20150024945 A1) in further view of Stefik et al. (US 20180322172 A1) still in further view of Schimmelpfeng et al. (US 20180011771 A1) still in further view of Kurian et al. (US 20170366394 A1).
As per claim 3, Chen, Healy and Stefik teach all the limitations as discussed in claim 2 above.
However, it is noted that the combination of the prior arts of Chen, Healy and Stefik do not explicitly teach “wherein the management action to mitigate the movement includes at least one of forecasting and preparing for at least one of: a reallocation of resources into an account linked to the digital records; and the reallocation of resources out of the account linked to the digital records.”
On the other hand, in the same field of endeavor, Kurian teaches wherein the management action to mitigate the movement includes at least one of forecasting and preparing for at least one of: a reallocation of resources into an account linked to the digital records (i.e. “the resource reallocation system 400 may be configured to initially allocate the resources to one or more resource blocks”; para. [0077]); and
the reallocation of resources out of the account linked to the digital records (i.e. “a resource transfer protocol that automatically transfers each resource block to the custodian's spouse upon the custodian's death and”; para. [0079]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Kurian that teaches a resource reallocation system into the combination of the prior arts of Chen that teaches automatic item placement recommendation, Healy that teaches generating a plurality of target partitions, Stefik that teaches generating recurring events, and Schimmelpfeng that teaches monitoring changes to an application. Additionally, this can improve the efficiency of a computing device implementing the item placement configuration system while simultaneously reducing an amount of computational resources required by the computing device to do so.
The motivation for doing so would be to ensure timely and efficient execution of the resource transfer protocol, thereby optimizing resource utilization and minimizing delays (Kurian, para. [0001], [0051]).
As per claim 4, Chen, Healy, Stefik and Kurian teach all the limitations as discussed in claim 2 above.
However, it is noted that the combination of the prior arts of Chen, Healy and Stefik do not explicitly teach “wherein initiating the management action comprises releasing a gate to at least one of: initiate a reallocation of resources into an account linked to the digital records; and initiate the reallocation of resources out of the account linked to the digital records.”
On the other hand, in the same field of endeavor, Kurian teaches wherein initiating the management action comprises releasing a gate to at least one of: initiate a reallocation of resources into an account linked to the digital records (i.e. “the resource reallocation system 400 may be configured to initially allocate the resources to one or more resource blocks”; para. [0076]-[0077], [0083]-[0084]); and
initiate the reallocation of resources out of the account linked to the digital records (i.e. “a resource transfer protocol that automatically transfers each resource block to the custodian's spouse upon the custodian's death and”; para. [0079]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Kurian that teaches a resource reallocation system into the combination of the prior arts of Chen that teaches automatic item placement recommendation, Healy that teaches generating a plurality of target partitions, Stefik that teaches generating recurring events, and Schimmelpfeng that teaches monitoring changes to an application. Additionally, this can improve the efficiency of a computing device implementing the item placement configuration system while simultaneously reducing an amount of computational resources required by the computing device to do so.
The motivation for doing so would be to ensure timely and efficient execution of the resource transfer protocol, thereby optimizing resource utilization and minimizing delays (Kurian, para. [0001], [0051]).
As per claim 5, Chen, Healy, Stefik and Kurian teach all the limitations as discussed in claim 4 above.
However, it is noted that the combination of the prior arts of Chen, Healy and Stefik do not explicitly teach “wherein the resources are at least one of monetary funds and other digitally represented assets.”
On the other hand, in the same field of endeavor, Kurian teaches wherein the resources are at least one of monetary funds and other digitally represented assets (i.e. “resources of the custodian may include accounts (e.g., a checking account, savings account, or investment account) of the custodian that are maintained by the entity, accounts of the custodian that are maintained by other entities (e.g., other financial institutions), an insurance policy of the custodian, and/or personal property of the custodian, such as smart devices.”; para. [0076]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Kurian that teaches a resource reallocation system into the combination of the prior arts of Chen that teaches automatic item placement recommendation, Healy that teaches generating a plurality of target partitions, Stefik that teaches generating recurring events, and Schimmelpfeng that teaches monitoring changes to an application. Additionally, this can improve the efficiency of a computing device implementing the item placement configuration system while simultaneously reducing an amount of computational resources required by the computing device to do so.
The motivation for doing so would be to ensure timely and efficient execution of the resource transfer protocol, thereby optimizing resource utilization and minimizing delays (Kurian, para. [0001], [0051]).
As per claim 13, Chen, Healy and Stefik teach all the limitations as discussed in claim 12 above.
However, it is noted that the combination of the prior arts of Chen, Healy and Stefik do not explicitly teach “wherein the management action to mitigate the movement includes at least one of forecasting and preparing for at least one of: a reallocation of resources into an account linked to the digital records; and the reallocation of resources out of the account linked to the digital records.”
On the other hand, in the same field of endeavor, Kurian teaches wherein the management action to mitigate the movement includes at least one of forecasting and preparing for at least one of: a reallocation of resources into an account linked to the digital records (i.e. “the resource reallocation system 400 may be configured to initially allocate the resources to one or more resource blocks”; para. [0077]); and
the reallocation of resources out of the account linked to the digital records (i.e. “a resource transfer protocol that automatically transfers each resource block to the custodian's spouse upon the custodian's death and”; para. [0079]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Kurian that teaches a resource reallocation system into the combination of the prior arts of Chen that teaches automatic item placement recommendation, Healy that teaches generating a plurality of target partitions, Stefik that teaches generating recurring events, and Schimmelpfeng that teaches monitoring changes to an application. Additionally, this can improve the efficiency of a computing device implementing the item placement configuration system while simultaneously reducing an amount of computational resources required by the computing device to do so.
The motivation for doing so would be to ensure timely and efficient execution of the resource transfer protocol, thereby optimizing resource utilization and minimizing delays (Kurian, para. [0001], [0051]).
As per claim 14, Chen, Healy and Stefik teach all the limitations as discussed in claim 12 above.
However, it is noted that the combination of the prior arts of Chen, Healy and Stefik do not explicitly teach “wherein initiating the management action comprises releasing a gate to at least one of: initiate a reallocation of resources into an account linked to the digital records; and initiate the reallocation of resources out of the account linked to the digital records.”
On the other hand, in the same field of endeavor, Kurian teaches wherein initiating the management action comprises releasing a gate to at least one of: initiate a reallocation of resources into an account linked to the digital records (i.e. “the resource reallocation system 400 may be configured to initially allocate the resources to one or more resource blocks”; para. [0076]-[0077], [0083]-[0084]); and
initiate the reallocation of resources out of the account linked to the digital records (i.e. “a resource transfer protocol that automatically transfers each resource block to the custodian's spouse upon the custodian's death and”; para. [0079]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Kurian that teaches a resource reallocation system into the combination of the prior arts of Chen that teaches automatic item placement recommendation, Healy that teaches generating a plurality of target partitions, Stefik that teaches generating recurring events, and Schimmelpfeng that teaches monitoring changes to an application. Additionally, this can improve the efficiency of a computing device implementing the item placement configuration system while simultaneously reducing an amount of computational resources required by the computing device to do so.
The motivation for doing so would be to ensure timely and efficient execution of the resource transfer protocol, thereby optimizing resource utilization and minimizing delays (Kurian, para. [0001], [0051]).
As per claim 15, Chen, Healy, Stefik and Kurian teach all the limitations as discussed in claim 14 above.
However, it is noted that the combination of the prior arts of Chen, Healy and Stefik do not explicitly teach “wherein the resources are at least one of monetary funds and other digitally represented assets.”
On the other hand, in the same field of endeavor, Kurian teaches wherein the resources are at least one of monetary funds and other digitally represented assets (i.e. “resources of the custodian may include accounts (e.g., a checking account, savings account, or investment account) of the custodian that are maintained by the entity, accounts of the custodian that are maintained by other entities (e.g., other financial institutions), an insurance policy of the custodian, and/or personal property of the custodian, such as smart devices.”; para. [0076]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Kurian that teaches a resource reallocation system into the combination of the prior arts of Chen that teaches automatic item placement recommendation, Healy that teaches generating a plurality of target partitions, Stefik that teaches generating recurring events, and Schimmelpfeng that teaches monitoring changes to an application. Additionally, this can improve the efficiency of a computing device implementing the item placement configuration system while simultaneously reducing an amount of computational resources required by the computing device to do so.
The motivation for doing so would be to ensure timely and efficient execution of the resource transfer protocol, thereby optimizing resource utilization and minimizing delays (Kurian, para. [0001], [0051]).
10. Claims 8-9 and 18-19 are rejected under 35 U.S.C. § 103 as being unpatentable over Chen et al. (US 20210110432 A1) in view of Healy (US 20150024945 A1) in further view of Stefik et al. (US 20180322172 A1) still in further view of Jacob et al. (US 20210397639 A1).
As per claim 8, Chen, Healy and Stefik teach all the limitations as discussed in claim 1 above.
However, it is noted that the combination of the prior arts of Chen, Healy and Stefik do not explicitly teach “further comprising, prior to the logic to transform the digital records from the disparate computer server systems into visualizations and anchor tags, logic for: processing the digital records through a parser to generate a large sample set, wherein each sample in the large sample set comprises a sequence of one or more symbols; high-pass filtering the large sample set to reduce its contents to the highest-frequency components, resulting in a filtered sample set; and utilizing the filtered sample set as the basis for mapping the digital records to the feature vectors in the higher-dimensional space.”
On the other hand, in the same field of endeavor, Jacob teaches further comprising, prior to the logic to transform the digital records from the disparate computer server systems into visualizations and anchor tags, logic for: processing the digital records through a parser to generate a large sample set (i.e. “The document-term matrix may specify the frequency of each term in the term dictionary for each case record within the set of case records. The LDA model 208 is generated by using the document-term matrix to identify a set of topics.”; para. [0033]; Examiner note: the large sample set is interpreted as the set of topics),
wherein each sample in the large sample set comprises a sequence of one or more symbols (i.e. “Each cluster C1-C5 includes a corresponding subset of topics defined by the LDA model 208.”; para. [0035]);
high-pass filtering the large sample set to reduce its contents to the highest-frequency components, resulting in a filtered sample set (i.e. “The user input may include one or more filter parameters associated with the selected cluster. Filter parameters may include search parameters, search terms, or other user selects to narrow down a comprehensive set of case records into a smaller subset of case records.”; para. [0052]); and
utilizing the filtered sample set as the basis for mapping the digital records to the feature vectors in the higher-dimensional space (i.e. “the computing system identifies a subset of case records associated with the selected cluster according to the filter parameters.”; para. [0053]. Further, i.e. “Each topic defined by the LDA model 208 may be converted into a feature vector and then clustered according to a cluster number. FIG. 2 shows each topic mapped onto a visual representation of a feature vector space.”; para. [0035]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Jacob that teaches systems and methods for receiving a set analyzing case records by extracting case text, performing natural language processing, and allocating each case text to a topics into the combination of the prior arts of Chen that teaches automatic item placement recommendation, Healy that teaches generating a plurality of target partitions, and Stefik that teaches generating recurring events. Additionally, this can improve the efficiency of a computing device implementing the item placement configuration system while simultaneously reducing an amount of computational resources required by the computing device to do so.
The motivation for doing so would be to use NLP to analyze large datasets is to efficiently extract actionable insights from unstructured textual information, enabling faster decision-making, enhanced automation, and improved understanding of human communication (Jacob, para. [0003]-[0005]).
As per claim 9, Chen, Healy and Stefik teach all the limitations as discussed in claim 1 above.
Additionally, Chen teaches applying a labeling algorithm to the clusters of interest to generate labeled clusters of feature vectors in the higher than three-dimensional vector space (i.e. “the taxonomy module 114 may identify a first plurality of items having associated descriptions indicating that the respective items can be categorized as “Baby & Toddler Clothing” items, identify a second plurality of items having associated descriptions indicating that the respective items can be categorized as “baby & toddler boy clothing” items, and identify a third plurality of items having associated descriptions indicating that the respective items can be categorized as “newborn outfit” items.”; para. [0052]; Examiner note: the taxonomy module is read to the labeling algorithm), wherein the labeling algorithm comprises at least one of: Natural Language Processing algorithms; Natural Language Understanding algorithms; topical analysis algorithms; and subject analysis algorithms (i.e. “the taxonomy module 114 may incorporate any suitable type of natural language processing, such as stop-word removal and stemming, Word2Vec, k-means, combinations thereof, and so forth, to categorize various items used to generate the multi-domain taxonomy 202.”; para. [0052],
However, it is noted that the combination of the prior arts of Chen, Healy and Stefik do not explicitly teach “wherein forming labeled clusters of the vectors comprises: providing the DBSCAN algorithm with: a minimum number of data points that make up a cluster; and a maximum distance between points in order to merge it into the cluster, wherein the maximum distance is determined at least in part from the Hamming distances; receiving clusters of interest after executing the DBSCAN algorithm; and passing the clusters of interest through a summary stage comprising:”
On the other hand, in the same field of endeavor, Jacob teaches wherein forming labeled clusters of the vectors comprises: providing the DBSCAN algorithm with: a minimum number of data points that make up a cluster (i.e. “An exemplary hierarchical clustering algorithm is the hierarchical agglomerative (HAC) algorithm. In HAC, each data point is initially regarded as an individual cluster”; para. [0051]); and
a maximum distance between points in order to merge it into the cluster (i.e. “and then the task is to iteratively combine two smaller clusters into a larger one based on the distance between their data points.”; para. [0051]; Examiner note: the maximum distance between points is interpreted as the distance between their data points),
wherein the maximum distance is determined at least in part from the Hamming distances (i.e. “Various other distance metrics can be used in the invention including Manhattan distance, maximum norm, Mahalanobis distance, and Hamming distance”; para. [0061]);
receiving clusters of interest after executing the DBSCAN algorithm (i.e. “The invention utilizes a clustering algorithm such as the K-means algorithm to cluster defect reports.”; para. [0044]. Further, i.e. “The clustering algorithm can be K-means, Expectation-Maximization, FarthestFirst, a hierarchical clustering algorithm, a density-based clustering algorithm, a grid-based clustering algorithm, a subspace clustering algorithm, a graph-partitioning algorithms, fuzzy c-means, DENCLUE, hierarchical agglomerative, DBSCAN,”; para. [0007]; Examiner note: the cluster of interest is interpreted as the defect reports); and
passing the clusters of interest through a summary stage (i.e. “Once the defect report is preprocessed, the defect reports are passed to representation module 210. Representation module 210 can convert the defect report into a representation format such as the vector space model. The representation module can also apply a weighting scheme such as the TF-IDF scheme.”; para. [0068]) comprising:
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Jacob that teaches systems and methods for receiving a set analyzing case records by extracting case text, performing natural language processing, and allocating each case text to a topics into the combination of the prior arts of Chen that teaches automatic item placement recommendation, Healy that teaches generating a plurality of target partitions, and Stefik that teaches generating recurring events. Additionally, this can improve the efficiency of a computing device implementing the item placement configuration system while simultaneously reducing an amount of computational resources required by the computing device to do so.
The motivation for doing so would be to use NLP to analyze large datasets is to efficiently extract actionable insights from unstructured textual information, enabling faster decision-making, enhanced automation, and improved understanding of human communication (Jacob, para. [0003]-[0005]).
As per claim 18, Chen, Healy and Stefik teach all the limitations as discussed in claim 11 above.
However, it is noted that the combination of the prior arts of Chen, Healy and Stefik do not explicitly teach “further comprising, prior to transforming the digital records from the disparate computer server systems into visualizations and anchor tags: processing the digital records through a parser to generate a large sample set, wherein each sample in the large sample set comprises a sequence of one or more symbols; high-pass filtering the large sample set to reduce its contents to the highest-frequency components, resulting in a filtered sample set; and utilizing the filtered sample set as the basis for mapping the digital records to the feature vectors in the higher-dimensional space.”
On the other hand, in the same field of endeavor, Jacob teaches further comprising, prior to transforming the digital records from the disparate computer server systems into visualizations and anchor tags: processing the digital records through a parser to generate a large sample set (i.e. “The document-term matrix may specify the frequency of each term in the term dictionary for each case record within the set of case records. The LDA model 208 is generated by using the document-term matrix to identify a set of topics.”; para. [0033]; Examiner note: the large sample set is interpreted as the set of topics),
wherein each sample in the large sample set comprises a sequence of one or more symbols (i.e. “Each cluster C1-C5 includes a corresponding subset of topics defined by the LDA model 208.”; para. [0035]);
high-pass filtering the large sample set to reduce its contents to the highest-frequency components, resulting in a filtered sample set (i.e. “The user input may include one or more filter parameters associated with the selected cluster. Filter parameters may include search parameters, search terms, or other user selects to narrow down a comprehensive set of case records into a smaller subset of case records.”; para. [0052]); and
utilizing the filtered sample set as the basis for mapping the digital records to the feature vectors in the higher-dimensional space (i.e. “the computing system identifies a subset of case records associated with the selected cluster according to the filter parameters.”; para. [0053]. Further, i.e. “Each topic defined by the LDA model 208 may be converted into a feature vector and then clustered according to a cluster number. FIG. 2 shows each topic mapped onto a visual representation of a feature vector space.”; para. [0035]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Jacob that teaches systems and methods for receiving a set analyzing case records by extracting case text, performing natural language processing, and allocating each case text to a topics into the combination of the prior arts of Chen that teaches automatic item placement recommendation, Healy that teaches generating a plurality of target partitions, and Stefik that teaches generating recurring events. Additionally, this can improve the efficiency of a computing device implementing the item placement configuration system while simultaneously reducing an amount of computational resources required by the computing device to do so.
The motivation for doing so would be to use NLP to analyze large datasets is to efficiently extract actionable insights from unstructured textual information, enabling faster decision-making, enhanced automation, and improved understanding of human communication (Jacob, para. [0003]-[0005]).
As per claim 19, Chen, Healy and Stefik teach all the limitations as discussed in claim 11 above.
Additionally, Chen teaches applying a labeling algorithm to the clusters of interest to generate labeled clusters of feature vectors in the higher than three-dimensional vector space (i.e. “the taxonomy module 114 may identify a first plurality of items having associated descriptions indicating that the respective items can be categorized as “Baby & Toddler Clothing” items, identify a second plurality of items having associated descriptions indicating that the respective items can be categorized as “baby & toddler boy clothing” items, and identify a third plurality of items having associated descriptions indicating that the respective items can be categorized as “newborn outfit” items.”; para. [0052]; Examiner note: the taxonomy module is read to the labeling algorithm), wherein the labeling algorithm comprises at least one of: Natural Language Processing algorithms; Natural Language Understanding algorithms; topical analysis algorithms; and subject analysis algorithms (i.e. “the taxonomy module 114 may incorporate any suitable type of natural language processing, such as stop-word removal and stemming, Word2Vec, k-means, combinations thereof, and so forth, to categorize various items used to generate the multi-domain taxonomy 202.”; para. [0052],
However, it is noted that the combination of the prior arts of Chen, Healy and Stefik do not explicitly teach “wherein forming labeled clusters of the vectors comprises: providing the DBSCAN algorithm with: a minimum number of data points that make up a cluster; and a maximum distance between points in order to merge it into the cluster, wherein the maximum distance is determined at least in part from the Hamming distances; receiving clusters of interest after executing the DBSCAN algorithm; and passing the clusters of interest through a summary stage comprising:”
On the other hand, in the same field of endeavor, Jacob teaches wherein forming labeled clusters of the vectors comprises: providing the DBSCAN algorithm with: a minimum number of data points that make up a cluster (i.e. “An exemplary hierarchical clustering algorithm is the hierarchical agglomerative (HAC) algorithm. In HAC, each data point is initially regarded as an individual cluster”; para. [0051]); and
a maximum distance between points in order to merge it into the cluster (i.e. “and then the task is to iteratively combine two smaller clusters into a larger one based on the distance between their data points.”; para. [0051]; Examiner note: the maximum distance between points is interpreted as the distance between their data points),
wherein the maximum distance is determined at least in part from the Hamming distances (i.e. “Various other distance metrics can be used in the invention including Manhattan distance, maximum norm, Mahalanobis distance, and Hamming distance”; para. [0061]);
receiving clusters of interest after executing the DBSCAN algorithm (i.e. “The invention utilizes a clustering algorithm such as the K-means algorithm to cluster defect reports.”; para. [0044]. Further, i.e. “The clustering algorithm can be K-means, Expectation-Maximization, FarthestFirst, a hierarchical clustering algorithm, a density-based clustering algorithm, a grid-based clustering algorithm, a subspace clustering algorithm, a graph-partitioning algorithms, fuzzy c-means, DENCLUE, hierarchical agglomerative, DBSCAN,”; para. [0007]; Examiner note: the cluster of interest is interpreted as the defect reports); and
passing the clusters of interest through a summary stage (i.e. “Once the defect report is preprocessed, the defect reports are passed to representation module 210. Representation module 210 can convert the defect report into a representation format such as the vector space model. The representation module can also apply a weighting scheme such as the TF-IDF scheme.”; para. [0068]) comprising:
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Jacob that teaches systems and methods for receiving a set analyzing case records by extracting case text, performing natural language processing, and allocating each case text to a topics into the combination of the prior arts of Chen that teaches automatic item placement recommendation, Healy that teaches generating a plurality of target partitions, and Stefik that teaches generating recurring events. Additionally, this can improve the efficiency of a computing device implementing the item placement configuration system while simultaneously reducing an amount of computational resources required by the computing device to do so.
The motivation for doing so would be to use NLP to analyze large datasets is to efficiently extract actionable insights from unstructured textual information, enabling faster decision-making, enhanced automation, and improved understanding of human communication (Jacob, para. [0003]-[0005]).
Prior Art of Record
11. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Guberman et al. (US 20230123574 A1), teaches methods and systems for intelligent editing of legal documents.
Jin et al. (US 20220019745 A1), teaches a method and an apparatus for training a service model, and a method and an apparatus for determining a text classification category.
Saha et al. (US 20220284045 A1), teaches a machine generated data entries are clustered into a plurality of different clusters that each includes a different subset of the received machine generated data entries.
Ghadar (US 11238070 B1), teaches a fast cluster-identifying algorithm can be used to find high density areas where certain less interesting content items might be clustered in a feature space.
Oliver et al. (US 11182481 B1), teaches evaluation of files for cyber threats.
Davidson et al. (US 20210081822 A1), teaches creation of training datasets for training image recognition algorithms.
Nadger et al. (US 20210026724 A1), teaches methods for performing root cause analysis for non-deterministic anomalies in a datacenter.
Li et al. (US 20190377794 A1), teaches method and apparatuses for determining user intents.
Carr et al. (US 20190346442 A1), teaches methods for improved prediction of HLA-peptide binding, datasets for predicting HLA-peptide binding and selection of HLA-binding peptides and compositions comprising HLA-binding peptides obtained by these methods.
Guggilla et al. (US 20190065991 A1), teaches a machine learning document processing system performs natural language processing (NLP) and machine learning to determine a subset of documents from a document dataset based on the structural features and semantic features.
Wood et al. (US 20140280146 A1), teaches clustering using tri-point arbitration.
Rhinelander et al. (US 20140250127 A1), teaches systems and methods for clustering content according to similarity are provided that identify and group similar content using a set of tags associated with the content.
Conclusion
12. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANTONIO CAIA DO whose telephone number is (469)295-9251. The examiner can normally be reached on Monday - Friday / 06:30 to 16:30.
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/ANTONIO J CAIA DO/
Examiner, Art Unit 2164
/MARK E HERSHLEY/Primary Examiner, Art Unit 2164