DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Office Action is responsive to Applicant's amendment filed on 23 June 2026. Applicant’s amendment on 23 June 2026 amended Claims 1, 4, 7, 10, 13, 14, 18 and 19. Currently Claims 1-20 are pending and have been examined. The Examiner notes that the 101 rejection has been maintained.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 23 June 2026 has been entered.
Response to Arguments
Applicant's arguments filed 23 June 2026 have been fully considered but they are not persuasive.
The Applicant argues on page 12 that the Examiner's characterization of the claims as directed to "time tracking which tracks personal productivity information and identify potential improvements" improperly abstracts away the specific technical features of the amended claims and is untethered from the claim language under Enfish.
The Examiner respectfully disagrees.
With regard to the argument, the Examiner notes that the quoted phrase was a summary provided for context and does not represent the entirety of the Step 2A, Prong One analysis. The controlling analysis, reproduced and elaborated below, is grounded in the specific claim language: obtaining activity data; generating a user graph of nodes and timestamped edges; selecting edges by timestamp and inducing first and second subgraphs; identifying a topic of interest from the nodes/edges of those subgraphs; determining time spent on the topic from edge timestamps; determining a predicted future time from the first and second amounts of time; and causing an identifier and the predicted amount to be displayed. Each of these operations is an observation, evaluation, comparison, or calculation that a person of ordinary skill would recognize as falling within the mental-process and mathematical-concept groupings of MPEP 2106.04(a)(2), as explained limitation-by-limitation below. Because the rejection is tied to specific claim limitations rather than a generalized label, the concern expressed in Enfish that a court (or examiner) not describe a claim at a level of abstraction untethered from the claim language is not implicated. Therefore, the rejection is maintained.
The Applicant argues on pages 12–13 that the amended claims recite "a specific technical data processing pipeline" for transforming heterogeneous, multi-application activity data into structured temporal graph representations, and that this pipeline "is not something practically performable in the human mind."
The Examiner respectfully disagrees.
With regard to the argument, the Examiner notes that the cases in which a claim was found not practically performable in the human mind e.g., SRI International, Inc. v. Cisco Systems, Inc., (analyzing raw network packets to detect suspicious activity), and SiRF Technology, Inc. v. International Trade Commission, (calculating GPS pseudo ranges from satellite signals) involve claim limitations requiring specialized signal- or packet-level computation that the human mind is not equipped to perform at all, regardless of time or assistance. By contrast, the claimed pipeline here reviewing a set of dated activities, sorting them into a time-bounded group, noting which entities or activities recur most frequently within that group and calling that a "topic," summing elapsed time between start and end timestamps associated with that topic, and projecting a future value from two prior period totals describes operations a person of ordinary skill would recognize as the type of collecting, filtering, and evaluating activity a human can and routinely does perform with a calendar, notebook, or spreadsheet. See MPEP 2106.04(a)(2), point B (a mental process performed with the aid of a physical tool such as pen and paper remains a mental process); Electric Power Group, LLC v. Alstom S.A., (collecting information from multiple sources, analyzing it, and displaying the results is a mental process even though a computer is nominally recited); CyberSource Corp. v. Retail Decisions, Inc. The claims recite no specific data volume, real-time constraint, or specialized technical computation (e.g., signal processing or cryptographic transformation) comparable to SRI or SiRF that would place the pipeline beyond the practical reach of the human mind merely because the source data happens to originate from multiple software applications. Therefore, the rejection is maintained.
The Applicant argues on page 13 that the "subgraph construction mechanics are not mental processes" because "a person cannot practically perform these operations mentally across data from multiple computer-implemented applications," specifically referencing the "selecting a first subset of edges... that have corresponding timestamps falling within a first time period" and "generating a first subgraph… based upon the first subset of edges" limitations.
The Examiner respectfully disagrees.
With regard to the argument, the Examiner notes that the number or diversity of source applications from which the underlying data is drawn does not, standing alone, remove an operation from the mental-process grouping; what governs the analysis is the character of the operation performed on the data, not the label attached to its origin. Filtering a list of dated entries to those falling within a given date range, and grouping the connected items that remain, is a filtering-and-grouping operation a person can perform with an index card system or a marked-up calendar irrespective of whether the underlying entries originated in an email client, a word processor, or a meeting application the application source is simply metadata attached to each entry, not a technical obstacle to the sorting operation itself. This reading is confirmed by the specification, which discloses that the "analytics application" applies only conventional, off-the-shelf computational tools page rank, HITS, regression, and classifier models (par. [0038]) without disclosing any new algorithm, indexing scheme, or data structure specifically needed to handle data drawn from a plurality of applications. Accordingly, one of ordinary skill would understand the "selecting" and "generating [a] subgraph" limitations as reciting mental filtering and grouping steps, or at most insignificant, generically-recited data-organizing activity under MPEP 2106.05(g), notwithstanding the multi-application source of the underlying data. Therefore, the rejection is maintained.
The Applicant argues on page 13 that the claims integrate the alleged abstract idea into a practical application because they recite "a particular technical solution" that improves upon conventional time-tracking applications, which the specification (par. [0001]) describes as having "limited functionality" because they track time only on a per-application or per-device basis.
The Examiner respectfully disagrees.
With regard to the argument, the Examiner notes that this argument conflates an improvement to the abstract idea itself with an improvement to the functioning of a computer or another technology or technical field, which is the showing required at Step 2A, Prong Two under MPEP 2106.04(d)(1), as clarified by Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB Sept. 26, 2025) (precedential). Desjardins confirms that the specification must describe, with sufficient detail for a person of ordinary skill to recognize it, an improvement to how the computer or system itself operates there, a specific training mechanism that reduced storage requirements and addressed "catastrophic forgetting" in continual learning models and that the claim must reflect that specific mechanism. Here, the deficiency identified in par. [0001] is that conventional time-tracking applications report time only per-application or per-device and thus have "limited usefulness" a limitation in the scope and organization of the information reported to the user, not a described deficiency in any computer's memory usage, processing speed, network architecture, or data-storage mechanism. The claimed solution building a graph, filtering it into time-bounded subgraphs, and computing and predicting topic-level durations from that filtered data produces a more informative report to the user, which is the paradigm of "collecting information, analyzing it, and displaying certain results of the collection and analysis" found insufficient in Electric Power Group. It is more analogous to Trading Technologies International v. IBG, (providing a trader more information to facilitate better decisions improves the business process, not the computer or technology), than to DDR Holdings, LLC v. Hotels.com, L.P., or BASCOM Global Internet Services v. AT&T Mobility LLC, in which the claimed arrangements changed how the underlying computer network itself operated. Therefore, the rejection is maintained.
The Applicant argues on page 13 that the claimed "graph-based representation enables aggregation of activity durations across heterogeneous applications using a unified relational structure," which is "not achievable using conventional per-application tracking systems," and is therefore analogous to the self-referential database table found eligible in Enfish.
The Examiner respectfully disagrees.
With regard to the argument, the Examiner notes that Enfish was found eligible because its specification explained, in specific technical detail, how a self-referential table stored all data types within a single table (rather than multiple related tables), and specifically how that structural change improved computer search speed, increased flexibility, and reduced storage requirements relative to the conventional relational-database model with the claims reciting the corresponding structural features that produced those benefits. Here, by contrast, par. [0004]–[0005] and [0030] of the specification describe the "user graph" only in generic terms nodes representing entities such as documents, people, emails, meetings, and tasks, connected by edges representing relationships and carrying timestamps which a person of ordinary skill would recognize as a conventional graph-database schema rather than a novel data structure. The specification identifies no specific technical shortcoming in existing graph-database technology (e.g., query latency, indexing overhead, or storage inefficiency) that the claimed timestamp-based edge selection and subgraph induction is designed to overcome; the only benefits identified in par. [0007] are that the user receives more complete productivity information and that search results can be ranked more relevantly benefits to the informational content produced by the abstract idea, not to the underlying graph-storage or retrieval technology. Absent a specification-level technical explanation of the kind present in Enfish, and a corresponding claim limitation reflecting it, this argument does not establish integration into a practical application. Therefore, the rejection is maintained.
The Applicant argues on page 13 that, even if the claims are found to recite an abstract idea under Step 2A, the specific combination of time-partitioned subgraph construction, topology-based topic identification, edge-timestamp-based time computation, and cross-subgraph prediction represents "a non-conventional ordered combination that is not well-understood, routine, or conventional" under Step 2B.
The Examiner respectfully disagrees.
With regard to the argument, the Examiner notes that each additional element, considered individually and as an ordered combination, describes well-understood, routine, and conventional computer-implemented data-analytics activity under MPEP 2106.05(d): obtaining activity data from applications is routine data collection; constructing a graph of nodes and timestamped edges is routine data organization; filtering that graph by a date range and inducing the connected remainder is functionally equivalent to a conventional database query with a timestamp-range condition; applying "page rank," "HITS," or "regression" models which the specification itself identifies as pre-existing, generic computational tools (par. [0038]) to identify topics and predict future values is routine application of known machine-learning and statistical techniques; and causing a result to be displayed is routine output activity under MPEP 2106.05(f). This sequence collect, graph, filter, analyze with generic models, display is the ordinary and expected order of operations for any data-analytics application and does not, unlike the non-generic, technically-motivated arrangement of filtering components at issue in BASCOM Global Internet Services v. AT&T Mobility LLC, reflect an unconventional technical arrangement that produces a benefit unavailable through the conventional application of these same generic tools in their ordinary order. Therefore, the rejection is maintained.
The Applicant argues on pages 15-16 that “the motivation to combine is insufficient. The Examiner provides only boilerplate motivation that the combination.
The Examiner respectfully disagrees.
In response to the arguments the Examiner points out the Examiner has provided a KSR rational which is well settled in the art to provide support for the combination of references. Specifically, the rationale to support a conclusion that the claim would have been obvious is that all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. KSR,; Sakraida v. AG Pro, Inc.; Anderson's-Black Rock, Inc. v. Pavement Salvage Co.; Great Atlantic & P. Tea Co. v. Supermarket Equipment Corp. "[I]t can be important to identify a reason that would have prompted a person of ordinary skill in the relevant field to combine the elements in the way the claimed new invention does." KSR. If any of these findings cannot be made, then this rationale cannot be used to support a conclusion that the claim would have been obvious to one of ordinary skill in the art. The examiner provides a KSR rational as to why the combination of old elements merely would have performed the same function as it did separately and further for additional support provides a benefit as to combine the references with along with reasons for why it would be obvious to combine. Therefore, the KSR rationale used to provide motivation to combine is in fact sufficient.
Furthermore, the Supreme Court made clear in KSR that when considering obviousness, “the analysis need not seek out precise teachings directed to the specific subject matter of the challenged claim, for a court can take account of the inferences and creative steps that a person of ordinary skill in the art would employ.” KSR Inti Co. v. Teleflex, Inc. The rejection is therefore maintained.
The remaining Applicant argues filed on 23 June 2026 have been fully considered but they are moot in view of new grounds of rejection as necessitated by amendment.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter because the claim(s) 1-20 as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. The claim(s) 1-20 is/are directed to is directed to an abstract idea without significantly more. The claims recite a combination of mental processes (observing, evaluating, and organizing user activity data by time period and topic) and mathematical concepts (calculating elapsed time from timestamps and predicting a future time value using regression-based calculation) see Step 2A Prong One analysis below. This judicial exception is not integrated into a practical application because the additional elements amount to no more than generic computer components (a processor, memory, and a generically-recited "ML engine") used as tools to gather, organize, and output the results of the abstract idea, without any specific technical improvement to the functioning of a computer or another technology or technical field see Step 2A Prong Two analysis below. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, individually and in combination, constitute well-understood, routine, conventional data-gathering, data-storage, and data-output activity see Step 2B analysis below.
Step 1
Regarding Step 1 of the Subject Matter Eligibility Test for Products and Processes, claims 13-17 are directed to a process/method, claims 18-20 are directed to a non-transitory computer-readable storage medium (manufacture), and claims 1-12 are directed to a computing system (machine). Therefore, the claims fall within the statutory categories of invention.
Step 2A Prong 1
The claims recite an abstract idea. Specifically, independent claims 1, 13, and 18 recite the following limitations:
obtaining user activity data indicative of activity of a user across a plurality of computer-implemented applications;
generating a user graph of the user based upon the user activity data, comprising nodes and edges connecting the nodes representing activities performed by the user, wherein each edge comprises a timestamp;
selecting a first subset of edges in the user graph having timestamps falling within a first time period;
generating a first subgraph based upon the first subset of edges, comprising a first subset of nodes connected via the first subset of edges, representing first activities during the first time period;
selecting a second subset of edges having timestamps falling within a second time period;
generating a second subgraph based upon the second subset of edges, comprising a second subset of nodes connected via the second subset of edges, representing second activities during the second time period;
identifying, via a machine learning (ML) engine, a topic of interest to the user based upon the first nodes/edges of the first subgraph and the second nodes/edges of the second subgraph;
determining, via the ML engine, a first amount of time spent on the topic during the first time period based upon timestamps of the first subgraph's edges, and a second amount of time spent on the topic during the second time period based upon timestamps of the second subgraph's edges;
determining, via the ML engine, a third amount of time predicted to be spent on the topic during a future time period, based upon the first and second amounts of time; and
causing an identifier for the topic and an indication of the third amount of time to be displayed to the user.
Abstract Idea Grouping Analysis
Mental Process:
These limitations, under their broadest reasonable interpretation, cover performance in the human mind, including observation, evaluation, and judgment, with or without the aid of pen and paper. Specifically: the step of "generating a user graph... wherein the nodes and the edges represent activities performed by the user" encompasses a person mentally (or with pen and paper) recording a set of dated activities and the relationships among them, exactly as a person keeps a journal, calendar, or contact log; the steps of "selecting a first [or second] subset of edges... that have corresponding timestamps falling within a first [or second] time period" and "generating a first [or second] subgraph... based upon the first [or second] subset of edges" encompass a person reviewing that dated list, mentally sorting entries into a given date range, and noting which entries remain connected to one another an operation no different in kind from a person circling all calendar entries from a given week and noting which people, documents, or meetings they connect to; the step of "identifying... a topic of interest... based upon [the] nodes and edges" encompasses a person observing which entity or activity recurs most frequently among the circled entries and judging that entity to be the "topic" the user was focused on, an evaluation and judgment a human is fully capable of performing; and the steps of "determining... a first amount of time" and "a second amount of time... based upon timestamps" encompass a person manually computing elapsed time between a start and end notation for each dated entry and adding those durations together, precisely as a person tallies hours from a timesheet with pen and paper. The mere nominal recitation that these observations, evaluations, and calculations are performed "via a machine learning (ML) engine" does not take the claim limitations out of the mental processes grouping, because the claims do not recite any particular machine-learning architecture, training methodology, or algorithm the specification confirms that the "ML engine" is simply a collection of pre-existing, generic models (page rank, HITS, regression, and classifier models; par. [0038]) invoked as a tool to perform what is otherwise an observation/evaluation process. See MPEP 2106.04(a)(2), subsection III; see also 2024 AI Subject Matter Eligibility Update, Example 47 (recitation of a generic AI/ML tool does not negate the mental nature of an otherwise mental limitation where the claim does not specify the particular technical manner in which the tool performs the operation).
Mathematical Concept:
These limitations additionally, or in the alternative, recite mathematical calculations. Specifically, "determining... a first amount of time the user spent on activities for the topic of interest... based upon timestamps" and "a second amount of time... based upon timestamps" recite calculating a numerical duration by subtracting and summing timestamp values (see Spec. par. [0064]-[0066], describing that the analytics application "computes the amount of time... by taking a difference between the second timestamp and the first timestamp" and "sums the first duration... and the second duration"). Further, "determining... a third amount of time that the user is predicted to spend... based upon the first amount of time and the second amount of time" recites a mathematical calculation, as confirmed by the specification's express disclosure that this step is performed "using regression analysis" (Spec. par. [0067]). See MPEP 2106.04(a)(2), subsection I.
Because the claims recite the mental-process limitations and the mathematical-concept limitations as a single, integrated data-analysis scheme (collecting activity data, organizing it by time period, evaluating it to identify a topic, and calculating time totals and a predicted future total for that topic), these limitations are treated together as a single abstract idea for purposes of the remaining analysis. See MPEP 2106.04, subsection II.B.
Step 2A Prong 2
Identification of Additional Elements
The claims recite the following additional elements beyond the identified abstract idea:
a processor and memory storing instructions (claim 1); a processor (claim 13); a non-transitory computer-readable storage medium and a processor (claim 18);
"obtaining user activity data... across a plurality of computer-implemented applications" (data-gathering step);
the recitation that the identifying/determining steps are performed "via a machine learning (ML) engine" (generic computer component);
"causing an identifier for the topic and an indication of the third amount of time to be displayed... within a graphical user interface (GUI)" (claim 13) or "to be displayed to the user" (claims 1, 18) (data-output step).
Analysis of Additional Elements
Improvement to Technology or Technical Field (MPEP 2106.05(a)):
The claims do not recite an improvement to the functioning of a computer or to any other technology or technical field. The specification identifies the problem addressed as the "limited functionality" and "limited usefulness" of conventional time-tracking applications that report time only on a per-application or per-device basis (Spec. par. [0001]), and identifies the benefit of the claimed invention as providing the user "a more complete view of how his/her efforts are actually being spent over time" and enabling more relevant search-result ranking (Spec. par. [0007]). These are improvements to the scope and quality of the information delivered to the user i.e., improvements to the abstract idea of productivity analytics itself not improvements to how a computer stores data, processes data more quickly, uses less memory, or operates more efficiently. The specification discloses the "user graph" only as a conventional graph schema of nodes representing entities (documents, people, emails, meetings, tasks) and edges representing relationships and timestamps (Spec. par. [0004]-[0005], [0030]), with no disclosed technical improvement to graph-database storage, indexing, or retrieval mechanisms analogous to the self-referential table structure found eligible in Enfish, LLC v. Microsoft Corp.,. Nor does the specification identify any specific mechanism by which the machine-learning engine itself is improved, of the kind found sufficient in Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB Sept. 26, 2025) (precedential) (specific mechanism for preserving prior-task performance while learning new tasks); here, the specification discloses only that the ML engine applies generic, pre-existing models (page rank, HITS, regression, classifier models; Spec. par. [0038]) without modification. Accordingly, the claimed invention uses generic computer components as tools to perform the abstract idea of collecting, organizing, and analyzing productivity data more efficiently than a human could unaided, rather than improving the computer or technology itself. See MPEP 2106.05(a).
Particular Machine (MPEP 2106.05(b)):
The claims do not recite use of a particular machine that imposes meaningful limits on the claim. The recited "processor," "memory," "non-transitory computer-readable storage medium," and "ML engine" are generic computing components recited functionally and at a high level of generality (see Spec. par. [0028]-[0029], describing the computing system generically as "a cloud-based computing platform" or "a server computing device"), and do not impose meaningful limits on the scope of the claim.
Mere Instructions to Apply the Exception (MPEP 2106.05(f)):
The additional elements amount to no more than mere instructions to implement the abstract idea on a computer. The claims recite generic computing components (processor, memory, ML engine) performing generic computing functions (obtaining data, organizing data into a graph, filtering data, evaluating data, calculating durations, causing display) at a high level of generality, without specifying any particular technical manner of implementation. This is tantamount to adding the words "apply it" or "apply it with a machine-learning engine" to the judicial exception. See Alice Corp. v. CLS Bank Int'l.
Insignificant Extra-Solution Activity (MPEP 2106.05(g)):
The additional elements of "obtaining user activity data... across a plurality of computer-implemented applications" and "causing... to be displayed to the user" constitute insignificant extra-solution activity. The "obtaining" step is mere data gathering that precedes the abstract analysis, and the "causing... to be displayed" step is mere outputting of the result of that analysis; both are necessary but insignificant data-gathering and data-outputting steps incidental to the primary abstract data-analysis process. See MPEP 2106.05(g).
Considering the additional elements individually and in combination, the claims as a whole do not integrate the judicial exception into a practical application. The additional elements do not impose any meaningful limits on practicing the abstract idea; they merely narrow it to a generic computing environment and add insignificant data-gathering and data-output steps. Accordingly, the claims are directed to an abstract idea.
Step 2B
As discussed with respect to Step 2A Prong Two, the additional elements in the claims amount to no more than mere instructions to apply the exception using generic computer components (processor, memory, ML engine) performing generic functions (obtaining, organizing, displaying). The same analysis applies in Step 2B mere instructions to apply an exception using generic computer components cannot provide an inventive concept. See MPEP 2106.05(f).
Well-Understood, Routine, Conventional Activity Analysis
The additional elements, when considered individually and in combination, are well-understood, routine, and conventional activities in the field. Specifically:
Obtaining/receiving user activity data from a plurality of applications – The courts have recognized receiving or gathering data from multiple sources as well-understood, routine, conventional activity. See MPEP 2106.05(d)(II), citing Symantec,; TLI Communications LLC v. AV Auto. LLC,; OIP Techs., Inc. v. Amazon.com, Inc.,); Electric Power Group, LLC v. Alstom S.A., (collecting information from multiple sources is well-understood, routine, conventional).
Generating and storing a graph data structure of nodes and timestamped edges; generating and storing subgraphs in a data store. The courts have recognized storing and organizing information in memory or a data store as well-understood, routine, conventional activity. See MPEP 2106.05(d)(II), citing Versata Dev. Group, Inc. v. SAP Am., Inc.; OIP Techs. This is further evidenced by the specification itself, which discloses the user graph and snapshot/subgraphs as being generated and maintained using conventional, generic data-store components (Spec. par. [0028]-[0029], [0035]) without describing any non-conventional storage or indexing technique.
Applying generic machine-learning/statistical models (page rank, HITS, regression, classifier models) to identify a topic and compute/predict time values – The specification itself establishes that these are pre-existing, generic, off-the-shelf computational models applied without modification (Spec. par. [0038]: "The plurality of computer-implemented models may include page rank models, Hyperlink-Induced Topic Search (HITS) models, behavior modeling models, topic identification models, predictive models, and/or ranking models. The plurality of computer-implemented models include regression models or classifier models."). This citation to the specification itself constitutes evidence under MPEP 2106.07(a), subsection III(A) that these models are well-understood, routine, conventional tools, generically invoked rather than technically improved upon.
Causing an identifier and a time value to be displayed to the user / within a GUI – The courts have recognized displaying or outputting the results of data collection and analysis as well-understood, routine, conventional activity. See MPEP 2106.05(d)(II); Electric Power Group, (presenting the results of collected and analyzed data, without more, is insufficient to add an inventive concept).
Considering the additional elements individually and in combination, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The ordered combination of obtaining data, organizing it into a graph, filtering by timestamp, applying generic ML models, and displaying the result is the ordinary and expected sequence for a generic data-analytics application, and does not, as an ordered combination, provide any technical benefit beyond the sum of these individually conventional steps. The claims are not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent may not be obtained through the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 4, 5, 7, 13, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rossi (U.S. Patent Publication 2021/0014124 A1) in view of Gutierrez et al. (U.S. Patent Publication 2022/0309037 A1) (proper support is found in provisional 63/191,852) (hereafter Gutierrez) in further view of Harding et al. (U.S. Patent 10,468,126 B1) (hereafter Harding).
Referring to Claim 1, A computing system, comprising:
a processor (see; par. [0081]-[0082] of Rossi teaches a computer readable memory and processor).
memory storing instructions that, when executed by the processor, cause the processor to perform acts comprising (see; par. [0081]-[0082] of Rossi teaches a computer readable memory and processor).
wherein each edge in the user graph comprises a timestamp (see; par. [0006] of Rossi teaches the edge in the graph indicating a time point (i.e. timestamp)).
selecting a first subset of edges in the user graph that have corresponding timestamps falling within a first time period (see; par. [0055] of Rossi teaches a selection of initial edges from recent edges, par. [0052] where the time associated with the edges have temporal values (i.e. time period)).
generating a first subgraph of the user graph based upon the first subset of edges, wherein the first subgraph comprises a first subset of nodes connected via the first subset of edges, wherein the first subgraph represents first activities performed by the user in the plurality of computer-implemented applications during the first time period (see; Abstract and par. [0016] of Rossi teaches a time value associated with edges and nodes including interaction time (i.e. time period) on a temporal specific graph (i.e. first subset), par. [0034]-[0036] where platform activity data temporal graph including nodes, where the online platform activity representing nodes and edges along with timestamp).
selecting a second subset of edges in the user graph that have corresponding timestamps falling within a second time period (see; par. [0035] of Rossi teaches selecting a second edge of the temporal graph associated with a specific timestamp (i.e. second time period)).
generating a second subgraph of the user graph based upon the second subset of edges, wherein the second subgraph comprises a second subset of nodes connected via the second subset of edges, wherein the second subgraph represents second activities performed by the user in the plurality of computer-implemented applications during the second time period (see; par. [0016] and par. [0035] of Rossi teaches nodes and edges represent on a temporal graphs corresponding to users, where the nodes connected to edges and the temporal values are represented on the temporal graph (i.e. time specific second subgraph)).
identifying, via a machine learning (ML) engine, a topic of interest to the user based upon first nodes and first edges included in the first subgraph and second nodes and second edges included in the second subgraph (see; par. [0006] of Rossi teaches a subset of depicting a temporal graph (i.e. time-based subgraph) including nodes and edges).
determining, via the ML engine, a first amount of time the user spent on activities for the topic of interest during the first time period based upon timestamps associated with edges of the first subgraph, and a second amount of time the user spent on activities for the topic of interest during the second time period based upon timestamps associated with edges of the second subgraph (see; par. [0016]-[0018] of Rossi teaches a machine learning engine used to manage tasks and a temporal graphs along edges to identify a sequence of nodes, par. [0049] activities associated with users including timestamp).
causing an identifier for the topic and an indication of the third amount of time to be displayed to the user (see; Abstract of Rossi teaches causing an identifier of nodes in sequence to generate a sequence of values (i.e. third amount of time)).
Rossi does not exactly disclose the following limitations, however,
Gutierrez teaches determining, via the ML engine, a third amount of time that the user is predicted to spend working on the topic during a future time period based upon the first amount of time and the second amount of time the user spent on the topic (see; par. [0557] of Gutierrez teaches determining the predicted intent of a user based on previously collected data about the work habits of the user over time (i.e. multiple times), par. [0685] where the data can be used to predict future activities of the user based on patterns, par. [0086] using machine learning techniques (support is found in provisional 63/191,852 in par. [0072] and pg. 55, section C.3.3)).
The Examiner notes that Rossi teaches similar to the instant application analysis system receiving network data in the form of a temporal graph. Specifically, Bagheri discloses the analysis system determines embeddings for the nodes in the temporal graph belonging to the same entity and it is therefore viewed as analogous art in the same field of endeavor. Additionally, Gutierrez teaches dynamic presentation of searchable contextual actions and data of users in a computing environment utilizing nodes as representation as it is comparable in certain respects to Rossi which analysis system receiving network data in the form of a temporal graph as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Rossi discloses the determining a community of users with similar temporal behavior and generate electrotonic content in the form of subgraphs with nodes and edges. However, Rossi fails to disclose determining, via the ML engine, a third amount of time that the user is predicted to spend working on the topic during a future time period based upon the first amount of time and the second amount of time the user spent on the topic.
Gutierrez discloses determining, via the ML engine, a third amount of time that the user is predicted to spend working on the topic during a future time period based upon the first amount of time and the second amount of time the user spent on the topic.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Rossi determining, via the ML engine, a third amount of time that the user is predicted to spend working on the topic during a future time period based upon the first amount of time and the second amount of time the user spent on the topic as taught by Gutierrez since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Rossi, and Gutierrez teach the collecting and analysis of data in order to maximize the utilization of resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Rossi in view of Gutierrez does not explicitly disclose the following limitations, however,
Harding teaches obtaining user activity data indicative of activity of a user across a plurality of computer-implemented applications (see; col. 3, line (42-54) of Harding teaches determining event data including time analytics across multiple data streams), and
generating a user graph of the user based upon the user activity data, wherein the user graph comprises nodes and edges connecting the nodes, wherein the nodes and the edges represent activities performed by the user in the plurality of computer-implemented applications (see; col. 11, line (62) – col. 12, line (8) of Harding teaches creating a graph that depicts activity context and an action of a user represented by a node).
The Examiner notes that Rossi teaches similar to the instant application analysis system receiving network data in the form of a temporal graph. Specifically, Rossi discloses the analysis system determines embeddings for the nodes in the temporal graph belonging to the same entity and it is therefore viewed as analogous art in the same field of endeavor. Additionally, Gutierrez teaches dynamic presentation of searchable contextual actions and data of users in a computing environment utilizing nodes as representation as it is comparable in certain respects to Rossi which analysis system receiving network data in the form of a temporal graph as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Harding teaches a clinical activity network to analyze data from sources including a customer database as it is comparable in certain respects to Rossi and Gutierrez which analysis system receiving network data in the form of a temporal graph as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Rossi and Gutierrez discloses the analysis system receiving network data in the form of a temporal graph. However, Rossi and Gutierrez fails to disclose obtaining user activity data indicative of activity of a user across a plurality of computer-implemented applications and generating a user graph of the user based upon the user activity data, wherein the user graph comprises nodes and edges connecting the nodes, wherein the nodes and the edges represent activities performed by the user in the plurality of computer-implemented applications.
Harding discloses obtaining user activity data indicative of activity of a user across a plurality of computer-implemented applications and generating a user graph of the user based upon the user activity data, wherein the user graph comprises nodes and edges connecting the nodes, wherein the nodes and the edges represent activities performed by the user in the plurality of computer-implemented applications.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Rossi and Gutierrez obtaining user activity data indicative of activity of a user across a plurality of computer-implemented applications and generating a user graph of the user based upon the user activity data, wherein the user graph comprises nodes and edges connecting the nodes, wherein the nodes and the edges represent activities performed by the user in the plurality of computer-implemented applications as taught by Harding since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Rossi, Gutierrez, and Harding teach the collecting and analysis of data in order to maximize the utilization of resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Referring to Claim 4, see discussion of claim 1 above, while Rossi in view of Gutierrez in further view of Harding teaches the system above, Rossi further discloses the following limitations,
the first subset of nodes comprises nodes in the user graph that are connected to the first subset of edges, and wherein the second subset of nodes comprises nodes in the user graph that are connected to the second subset of edges (see; Abstract and par. [0016] of Rossi teaches a time value associated with edges and nodes including interaction time (i.e. time period) on a temporal specific graph (i.e. first subset), par. [0034]-[0036] where platform activity data temporal graph including nodes, where the online platform activity representing nodes and edges along with timestamp).
Referring to Claim 5, see discussion of claim 1 above, while Rossi in view of Gutierrez in further view of Harding teaches the system above, Rossi further discloses the following limitations,
each node in the user graph represents: an entity associated with the user; or an activity of the user (see; Abstract of Rossi teaches each node represents an entity, par. [0017] further each node that represents an entity related to a user activity).
Referring to Claim 7, see discussion of claim 1 above, while Rossi in view of Gutierrez in further view of Harding teaches the system above, Rossi further discloses the following limitations,
identifying, via a ML engine, entities associated with the topic of interest during the first time period based upon the first data comprised by the first subgraph (see; par. [0055]-[0056] of Rossi teaches a model to identify initial edges and nodes during a specific time period and then creating, Abstract a temporal graph representing a subset of a specific time periods (i.e. subgraph), par. [0042] applied by a machine learning model).
causing identifiers for the entities to be displayed to the user (see; par. [0016] of Rossi teaches causing identifiers to be visible on a temporal graph for the user).
Referring to Claim 13, Rossi in view of Gutierrez in further view of Harding teaches method. Claim 13 recites the same or similar limitations as those addressed above in claim 1, Claim 13 is therefore rejected for the same reasons as set forth above in claim 1.
Referring to Claim 18, Rossi in view of Gutierrez in further view of Harding teaches non-transitory computer readable storage medium. Claim 18 recites the same or similar limitations as those addressed above in claim 1, Claim 18 is therefore rejected for the same reasons as set forth above in claim 1.
Claims 2, 3, 6, 8-12, 14-17, 19, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rossi (U.S. Patent Publication 2021/0014124 A1) in view of Gutierrez et al. (U.S. Patent Publication 2022/0309037 A1) (proper support is found in provisional 63/191,852) (hereafter Gutierrez) in further view of Harding et al. (U.S. Patent 10,468,126 B1) (hereafter Harding) in further view of Bagheri et al. (U.S. Patent Publication 2018/0075147 A1) (hereafter Bagheri).
Referring to Claim 2, see discussion of claim 1 above, while Rossi in view of Gutierrez in further view of Harding teaches the system above, Rossi in view of Gutierrez in further view of Harding does not explicitly disclose the following limitations, however,
Bagheri teaches the acts further comprising: causing an indication of the first amount of time and an identifier for the first time period to be displayed to the user (see; par. [0056] of Bagheri teaches a display that represents data including, par. [0054]-[0055] a time series topics from a specific time period), and
causing an indication of the second amount of time and an identifier for the second time period to be displayed to the user (see; par. [0054]-[0055] of Bagheri teaches a time series topics from a par. [0046] specific time period (i.e. second time)).
The Examiner notes that Rossi teaches similar to the instant application analysis system receiving network data in the form of a temporal graph. Specifically, Rossi discloses the analysis system determines embeddings for the nodes in the temporal graph belonging to the same entity and it is therefore viewed as analogous art in the same field of endeavor. Additionally, Gutierrez teaches dynamic presentation of searchable contextual actions and data of users in a computing environment utilizing nodes as representation as it is comparable in certain respects to Rossi which analysis system receiving network data in the form of a temporal graph as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Harding teaches a clinical activity network to analyze data from sources including a customer database as it is comparable in certain respects to Rossi and Gutierrez which analysis system receiving network data in the form of a temporal graph as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Bagheri teaches a temporal identification of latent user communities using electronic content as it is comparable in certain respects to Rossi, Gutierrez, and Harding which analysis system receiving network data in the form of a temporal graph as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Rossi, Gutierrez, and Harding discloses the analysis system receiving network data in the form of a temporal graph. However, Rossi, Gutierrez, and Harding fails to disclose the acts further comprising: causing an indication of the first amount of time and an identifier for the first time period to be displayed to the user and causing an indication of the second amount of time and an identifier for the second time period to be displayed to the user.
Bagheri discloses the acts further comprising: causing an indication of the first amount of time and an identifier for the first time period to be displayed to the user and causing an indication of the second amount of time and an identifier for the second time period to be displayed to the user.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Rossi, Gutierrez, and Harding the acts further comprising: causing an indication of the first amount of time and an identifier for the first time period to be displayed to the user and causing an indication of the second amount of time and an identifier for the second time period to be displayed to the user as taught by Bagheri since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Rossi, Gutierrez, and Harding teach the collecting and analysis of data in order to maximize the utilization of resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Referring to Claim 3, see discussion of claim 2 above, while Rossi in view of Gutierrez in further view of Harding in further view of Bagheri teaches the system above, Rossi in view of Gutierrez in further view of Harding does not explicitly disclose the following limitations, however,
Bagheri teaches wherein the indication of the first amount of time, the identifier for the first time period, the indication of the second amount of time, the identifier for the second time period, the indication of the third amount of time, an identifier for the future time period, and the identifier for the topic are displayed in a plot presented on a display (see; par. [0046]-[0050] of Baheri teaches multiple time periods (i.e. first, second and third) including multiple identified topics, Figure 7 shows an example of a plot of the occurrence of an action taken by users displayed on a graph).
The Examiner notes that Rossi teaches similar to the instant application analysis system receiving network data in the form of a temporal graph. Specifically, Rossi discloses the analysis system determines embeddings for the nodes in the temporal graph belonging to the same entity and it is therefore viewed as analogous art in the same field of endeavor. Additionally, Gutierrez teaches dynamic presentation of searchable contextual actions and data of users in a computing environment utilizing nodes as representation as it is comparable in certain respects to Rossi which analysis system receiving network data in the form of a temporal graph as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Harding teaches a clinical activity network to analyze data from sources including a customer database as it is comparable in certain respects to Rossi and Gutierrez which analysis system receiving network data in the form of a temporal graph as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Bagheri teaches a temporal identification of latent user communities using electronic content as it is comparable in certain respects to Rossi, Gutierrez, and Harding which analysis system receiving network data in the form of a temporal graph as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Rossi, Gutierrez, and Harding discloses the analysis system receiving network data in the form of a temporal graph. However, Rossi, Gutierrez, and Harding fails to disclose wherein the indication of the first amount of time, the identifier for the first time period, the indication of the second amount of time, the identifier for the second time period, the indication of the third amount of time, an identifier for the future time period, and the identifier for the topic are displayed in a plot presented on a display.
Bagheri discloses wherein the indication of the first amount of time, the identifier for the first time period, the indication of the second amount of time, the identifier for the second time period, the indication of the third amount of time, an identifier for the future time period, and the identifier for the topic are displayed in a plot presented on a display.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Rossi, Gutierrez, and Harding wherein the indication of the first amount of time, the identifier for the first time period, the indication of the second amount of time, the identifier for the second time period, the indication of the third amount of time, an identifier for the future time period, and the identifier for the topic are displayed in a plot presented on a display as taught by Bagheri since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Rossi, Gutierrez, Harding, and Bagheri teach the collecting and analysis of data in order to maximize the utilization of resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Referring to Claim 6, see discussion of claim 1 above, while Rossi in view of Gutierrez in further view of Harding teaches the system above, Rossi in view of Gutierrez in further view of Harding does not explicitly disclose the following limitations, however,
Bagheri teaches during the future time period, receiving a search query from a computing device operated by the user (see: par. [0013] of Bagheri teaches inputting a request from a user (i.e. search) during a time period providing a user analysis), and
executing a search over the user graph based upon search query (see: par. [0013] and par. [0061] of Bagheri teaches a request from a user (i.e. search) during a time period providing a user analysis), and
obtaining search results for the search (see; par. [0131] of Bagheri teaches obtaining results of analysis requested by the user (i.e. search results)), and
ranking the search results based upon the topic of interest and the third amount of time that the user is predicted to spend working on the topic of interest during the future time period (see; par. [0138]-[0139] of Bagheri teaches ranking recommendations related to temporal topics), and
causing a highest ranked search result in the search results to be presented on a display of the computing device (see; par. [0136]-[0139] of Bagheri teaches providing topically relevant recommendations that have been ranked, par. [0070] on a display).
The Examiner notes that Rossi teaches similar to the instant application analysis system receiving network data in the form of a temporal graph. Specifically, Rossi discloses the analysis system determines embeddings for the nodes in the temporal graph belonging to the same entity and it is therefore viewed as analogous art in the same field of endeavor. Additionally, Gutierrez teaches dynamic presentation of searchable contextual actions and data of users in a computing environment utilizing nodes as representation as it is comparable in certain respects to Rossi which analysis system receiving network data in the form of a temporal graph as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Harding teaches a clinical activity network to analyze data from sources including a customer database as it is comparable in certain respects to Rossi and Gutierrez which analysis system receiving network data in the form of a temporal graph as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Bagheri teaches a temporal identification of latent user communities using electronic content as it is comparable in certain respects to Rossi, Gutierrez, and Harding which analysis system receiving network data in the form of a temporal graph as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Rossi, Gutierrez, and Harding discloses the analysis system receiving network data in the form of a temporal graph. However, Rossi, Gutierrez, and Harding fails to disclose during the future time period, receiving a search query from a computing device operated by the user, executing a search over the user graph based upon search query, obtaining search results for the search, ranking the search results based upon the topic of interest and the third amount of time that the user is predicted to spend working on the topic of interest during the future time period, and causing a highest ranked search result in the search results to be presented on a display of the computing device.
Bagheri discloses during the future time period, receiving a search query from a computing device operated by the user, executing a search over the user graph based upon search query, obtaining search results for the search, ranking the search results based upon the topic of interest and the third amount of time that the user is predicted to spend working on the topic of interest during the future time period, and causing a highest ranked search result in the search results to be presented on a display of the computing device.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Rossi, Gutierrez, and Harding during the future time period, receiving a search query from a computing device operated by the user, executing a search over the user graph based upon search query, obtaining search results for the search, ranking the search results based upon the topic of interest and the third amount of time that the user is predicted to spend working on the topic of interest during the future time period, and causing a highest ranked search result in the search results to be presented on a display of the computing device as taught by Bagheri since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Rossi, Gutierrez, Harding, and Bagheri teach the collecting and analysis of data in order to maximize the utilization of resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Referring to Claim 8, see discussion of claim 7 above, while Rossi in view of Gutierrez in further view of Harding teaches the system above, Rossi in view of Gutierrez in further view of Harding does not explicitly disclose the following limitations, however,
Bagheri teaches wherein the entities include one or more of people; documents; emails; meetings; work areas; tasks; applications; locations; or key phrases (see; par. [0050] of Bagheri teaches one of the entities includes an event).
The Examiner notes that Rossi teaches similar to the instant application analysis system receiving network data in the form of a temporal graph. Specifically, Rossi discloses the analysis system determines embeddings for the nodes in the temporal graph belonging to the same entity and it is therefore viewed as analogous art in the same field of endeavor. Additionally, Gutierrez teaches dynamic presentation of searchable contextual actions and data of users in a computing environment utilizing nodes as representation as it is comparable in certain respects to Rossi which analysis system receiving network data in the form of a temporal graph as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Harding teaches a clinical activity network to analyze data from sources including a customer database as it is comparable in certain respects to Rossi and Gutierrez which analysis system receiving network data in the form of a temporal graph as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Bagheri teaches a temporal identification of latent user communities using electronic content as it is comparable in certain respects to Rossi, Gutierrez, and Harding which analysis system receiving network data in the form of a temporal graph as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Rossi, Gutierrez, and Harding discloses the analysis system receiving network data in the form of a temporal graph. However, Rossi, Gutierrez, and Harding fails to disclose wherein the entities include one or more of people; documents; emails; meetings; work areas; tasks; applications; locations; or key phrases.
Bagheri discloses wherein the entities include one or more of people; documents; emails; meetings; work areas; tasks; applications; locations; or key phrases.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Rossi, Gutierrez, and Harding d wherein the entities include one or more of people; documents; emails; meetings; work areas; tasks; applications; locations; or key phrases as taught by Bagheri since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Rossi, Gutierrez, Harding, and Bagheri teach the collecting and analysis of data in order to maximize the utilization of resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Referring to Claim 9, see discussion of claim 1 above, while Rossi in view of Gutierrez in further view of Harding teaches the system above, Rossi in view of Gutierrez in further view of Harding does not explicitly disclose the following limitations, however,
Bagheri teaches the third amount of time comprises a fourth amount of time and a fifth amount of time, wherein the fourth amount of time corresponds to a first type of activity that is predicted to be performed by the user with respect to the topic during the future time period, wherein the fifth amount of time corresponds to a second type of activity that is predicted to be performed by the user with respect to the topic during the future time period (see; par. [0046]-[0050] of Baheri teaches multiple time periods (i.e. fourth and fifth) including multiple identified topics related to activities, Figure 7 shows an example of a plot of the occurrence of an action taken by users displayed on a graph).
The Examiner notes that Rossi teaches similar to the instant application analysis system receiving network data in the form of a temporal graph. Specifically, Rossi discloses the analysis system determines embeddings for the nodes in the temporal graph belonging to the same entity and it is therefore viewed as analogous art in the same field of endeavor. Additionally, Gutierrez teaches dynamic presentation of searchable contextual actions and data of users in a computing environment utilizing nodes as representation as it is comparable in certain respects to Rossi which analysis system receiving network data in the form of a temporal graph as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Harding teaches a clinical activity network to analyze data from sources including a customer database as it is comparable in certain respects to Rossi and Gutierrez which analysis system receiving network data in the form of a temporal graph as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Bagheri teaches a temporal identification of latent user communities using electronic content as it is comparable in certain respects to Rossi, Gutierrez, and Harding which analysis system receiving network data in the form of a temporal graph as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Rossi, Gutierrez, and Harding discloses the analysis system receiving network data in the form of a temporal graph. However, Rossi, Gutierrez, and Harding fails to disclose the third amount of time comprises a fourth amount of time and a fifth amount of time, wherein the fourth amount of time corresponds to a first type of activity that is predicted to be performed by the user with respect to the topic during the future time period, wherein the fifth amount of time corresponds to a second type of activity that is predicted to be performed by the user with respect to the topic during the future time period.
Bagheri discloses the third amount of time comprises a fourth amount of time and a fifth amount of time, wherein the fourth amount of time corresponds to a first type of activity that is predicted to be performed by the user with respect to the topic during the future time period, wherein the fifth amount of time corresponds to a second type of activity that is predicted to be performed by the user with respect to the topic during the future time period.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Rossi, Gutierrez, and Harding the third amount of time comprises a fourth amount of time and a fifth amount of time, wherein the fourth amount of time corresponds to a first type of activity that is predicted to be performed by the user with respect to the topic during the future time period, wherein the fifth amount of time corresponds to a second type of activity that is predicted to be performed by the user with respect to the topic during the future time period as taught by Bagheri since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Rossi, Gutierrez, Harding, and Bagheri teach the collecting and analysis of data in order to maximize the utilization of resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Referring to Claim 10, see discussion of claim 1 above, while Rossi in view of Gutierrez in further view of Harding teaches the system above, Rossi in view of Gutierrez in further view of Harding does not explicitly disclose the following limitations, however,
Bagheri teaches subsequent to the future time period elapsing, selecting a subset of the edges in the user graph that have corresponding timestamps falling within the future time period (see; par. [0017] and par. [0084] of Bagheri teaches establishing future time period and creating graphs and provide timestamps for the future time period), and
generating a third subgraph based upon the subset of edges, wherein the third subgraph comprises a subset of nodes of the user graph, wherein the subset of nodes are connected via the subset of edges, wherein the third subgraph represents third activities performed by the user in the plurality of applications during the future time period (see; par. [0115]-[0118] of Bagheri teaches analyzing edges to a specific time period (i.e. third time period) in multiple time periods, par. [0017] for generating subgraphs providing edges and nodes that indicate connections to other nodes and edges), and
storing the third subgraph and an identifier for the future time period in a data store (see; par. [0084] of Bagheri teaches that a periodic future time period that identified topics related to the future time period, par. [0094] as well as saving one of multiple subgraphs (i.e. third)).
The Examiner notes that Rossi teaches similar to the instant application analysis system receiving network data in the form of a temporal graph. Specifically, Rossi discloses the analysis system determines embeddings for the nodes in the temporal graph belonging to the same entity and it is therefore viewed as analogous art in the same field of endeavor. Additionally, Gutierrez teaches dynamic presentation of searchable contextual actions and data of users in a computing environment utilizing nodes as representation as it is comparable in certain respects to Rossi which analysis system receiving network data in the form of a temporal graph as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Harding teaches a clinical activity network to analyze data from sources including a customer database as it is comparable in certain respects to Rossi and Gutierrez which analysis system receiving network data in the form of a temporal graph as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Bagheri teaches a temporal identification of latent user communities using electronic content as it is comparable in certain respects to Rossi, Gutierrez, and Harding which analysis system receiving network data in the form of a temporal graph as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Rossi, Gutierrez, and Harding discloses the analysis system receiving network data in the form of a temporal graph. However, Rossi, Gutierrez, and Harding fails to disclose subsequent to the future time period elapsing, selecting a subset of the edges in the user graph that have corresponding timestamps falling within the future time period, generating a third subgraph based upon the subset of edges, wherein the third subgraph comprises a subset of nodes of the user graph, wherein the subset of nodes are connected via the subset of edges, wherein the third subgraph represents third activities performed by the user in the plurality of applications during the future time period, and storing the third subgraph and an identifier for the future time period in a data store.
Bagheri discloses subsequent to the future time period elapsing, selecting a subset of the edges in the user graph that have corresponding timestamps falling within the future time period, generating a third subgraph based upon the subset of edges, wherein the third subgraph comprises a subset of nodes of the user graph, wherein the subset of nodes are connected via the subset of edges, wherein the third subgraph represents third activities performed by the user in the plurality of applications during the future time period, and storing the third subgraph and an identifier for the future time period in a data store.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Rossi, Gutierrez, and Harding subsequent to the future time period elapsing, selecting a subset of the edges in the user graph that have corresponding timestamps falling within the future time period, generating a third subgraph based upon the subset of edges, wherein the third subgraph comprises a subset of nodes of the user graph, wherein the subset of nodes are connected via the subset of edges, wherein the third subgraph represents third activities performed by the user in the plurality of applications during the future time period, and storing the third subgraph and an identifier for the future time period in a data store as taught by Bagheri since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Rossi, Gutierrez, Harding, and Bagheri teach the collecting and analysis of data in order to maximize the utilization of resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Referring to Claim 11, see discussion of claim 1 above, while Rossi in view of Gutierrez in further view of Harding teaches the system above, Rossi in view of Gutierrez in further view of Harding does not explicitly disclose the following limitations, however,
Bagheri teaches the plurality of applications include at least one of an email application; a real-time messaging application; a real-time meeting application; a word processing application; a spreadsheet application; a slideshow application; or a web browser (see; par. [0119] of Bagheri teaches sending emails).
The Examiner notes that Rossi teaches similar to the instant application analysis system receiving network data in the form of a temporal graph. Specifically, Rossi discloses the analysis system determines embeddings for the nodes in the temporal graph belonging to the same entity and it is therefore viewed as analogous art in the same field of endeavor. Additionally, Gutierrez teaches dynamic presentation of searchable contextual actions and data of users in a computing environment utilizing nodes as representation as it is comparable in certain respects to Rossi which analysis system receiving network data in the form of a temporal graph as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Harding teaches a clinical activity network to analyze data from sources including a customer database as it is comparable in certain respects to Rossi and Gutierrez which analysis system receiving network data in the form of a temporal graph as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Bagheri teaches a temporal identification of latent user communities using electronic content as it is comparable in certain respects to Rossi, Gutierrez, and Harding which analysis system receiving network data in the form of a temporal graph as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Rossi, Gutierrez, and Harding discloses the analysis system receiving network data in the form of a temporal graph. However, Rossi, Gutierrez, and Harding fails to disclose the plurality of applications include at least one of an email application; a real-time messaging application; a real-time meeting application; a word processing application; a spreadsheet application; a slideshow application; or a web browser.
Bagheri discloses the plurality of applications include at least one of an email application; a real-time messaging application; a real-time meeting application; a word processing application; a spreadsheet application; a slideshow application; or a web browser.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Rossi, Gutierrez, and Harding the plurality of applications include at least one of an email application; a real-time messaging application; a real-time meeting application; a word processing application; a spreadsheet application; a slideshow application; or a web browser as taught by Bagheri since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Rossi, Gutierrez, Harding, and Bagheri teach the collecting and analysis of data in order to maximize the utilization of resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Referring to Claim 12, see discussion of claim 1 above, while Rossi in view of Gutierrez in further view of Harding teaches the system above, Rossi in view of Gutierrez in further view of Harding does not explicitly disclose the following limitations, however,
Bagheri teaches wherein the topic of interest is a work area, an application, a location, or a person (see; par. [0095] of Bagheri teaches the topic of interest includes a user (i.e. a person)).
The Examiner notes that Rossi teaches similar to the instant application analysis system receiving network data in the form of a temporal graph. Specifically, Rossi discloses the analysis system determines embeddings for the nodes in the temporal graph belonging to the same entity and it is therefore viewed as analogous art in the same field of endeavor. Additionally, Gutierrez teaches dynamic presentation of searchable contextual actions and data of users in a computing environment utilizing nodes as representation as it is comparable in certain respects to Rossi which analysis system receiving network data in the form of a temporal graph as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Harding teaches a clinical activity network to analyze data from sources including a customer database as it is comparable in certain respects to Rossi and Gutierrez which analysis system receiving network data in the form of a temporal graph as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Bagheri teaches a temporal identification of latent user communities using electronic content as it is comparable in certain respects to Rossi, Gutierrez, and Harding which analysis system receiving network data in the form of a temporal graph as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Rossi, Gutierrez, and Harding discloses the analysis system receiving network data in the form of a temporal graph. However, Rossi, Gutierrez, and Harding fails to disclose wherein the topic of interest is a work area, an application, a location, or a person.
Bagheri discloses wherein the topic of interest is a work area, an application, a location, or a person.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Rossi, Gutierrez, and Harding wherein the topic of interest is a work area, an application, a location, or a person as taught by Bagheri since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Rossi, Gutierrez, Harding, and Bagheri teach the collecting and analysis of data in order to maximize the utilization of resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Referring to Claim 14, see discussion of claim 13 above, while Rossi in view of Gutierrez in further view of Harding teaches the method above, Rossi further discloses a method having the limitations of:
a second topic of interest to the user based upon the first data included in the first subgraph and the second data included in the second subgraph (see; par. [0006] of Rossi teaches a subset of depicting a temporal graph (i.e. time-based subgraph) including nodes and edges, par. [0017] utilizing a machine learning).
Rossi in view of Gutierrez in further view of Harding does not explicitly disclose the following limitation, however,
Bagheri teaches determining, a fourth amount of time the user spent on activities for the second topic of interest during the first time period and a fifth amount of time the user spent on activities for the second topic of interest during the second time period (see; par. [0015]-[0017] of Bagheri teaches determining topics (i.e. second topic) for the time periods, par. [0051]-[0055] where the topics that are determined are topics during different identified time periods (i.e. second time period) for specifically identified times (i.e. fifth amount)), and
determining, a sixth amount of time that the user is predicted to spend working on the second topic during the future time period based upon the fourth amount of time and the fifth amount of time (see; par. [0118] of Bagheri teaches determining during a future time (i.e. 6th time) interests in the future interests of the user (i.e. work), par. [0015]-[0017] where additional topics and content are determined for the designated or different time period), and
causing an identifier for the second topic and an indication of the sixth amount of time to be displayed within the GUI to the user concurrently with the identifier for the topic and the indication of the third amount of time (see; par. [0111] of Bagheri teaches a user topic viewed at a third time period of time, par. [0015]-[0017] where additional topics and content are determined for the designated or different time period (i.e. third amount of time)).
Bagheri does not explicitly disclose the following limitation, however,
The Examiner notes that Rossi teaches similar to the instant application analysis system receiving network data in the form of a temporal graph. Specifically, Rossi discloses the analysis system determines embeddings for the nodes in the temporal graph belonging to the same entity and it is therefore viewed as analogous art in the same field of endeavor. Additionally, Gutierrez teaches dynamic presentation of searchable contextual actions and data of users in a computing environment utilizing nodes as representation as it is comparable in certain respects to Rossi which analysis system receiving network data in the form of a temporal graph as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Harding teaches a clinical activity network to analyze data from sources including a customer database as it is comparable in certain respects to Rossi and Gutierrez which analysis system receiving network data in the form of a temporal graph as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Bagheri teaches a temporal identification of latent user communities using electronic content as it is comparable in certain respects to Rossi, Gutierrez, and Harding which analysis system receiving network data in the form of a temporal graph as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Rossi, Gutierrez, and Harding discloses the analysis system receiving network data in the form of a temporal graph. However, Rossi, Gutierrez, and Harding fails to disclose determining, a fourth amount of time the user spent on activities for the second topic of interest during the first time period and a fifth amount of time the user spent on activities for the second topic of interest during the second time period, and determining, a sixth amount of time that the user is predicted to spend working on the second topic during the future time period based upon the fourth amount of time and the fifth amount of time, and causing an identifier for the second topic and an indication of the sixth amount of time to be displayed within the GUI to the user concurrently with the identifier for the topic and the indication of the third amount of time.
Bagheri discloses determining, a fourth amount of time the user spent on activities for the second topic of interest during the first time period and a fifth amount of time the user spent on activities for the second topic of interest during the second time period, and determining, a sixth amount of time that the user is predicted to spend working on the second topic during the future time period based upon the fourth amount of time and the fifth amount of time, and causing an identifier for the second topic and an indication of the sixth amount of time to be displayed within the GUI to the user concurrently with the identifier for the topic and the indication of the third amount of time.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Rossi, Gutierrez, and Harding determining, a fourth amount of time the user spent on activities for the second topic of interest during the first time period and a fifth amount of time the user spent on activities for the second topic of interest during the second time period, and determining, a sixth amount of time that the user is predicted to spend working on the second topic during the future time period based upon the fourth amount of time and the fifth amount of time, and causing an identifier for the second topic and an indication of the sixth amount of time to be displayed within the GUI to the user concurrently with the identifier for the topic and the indication of the third amount of time as taught by Bagheri since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Rossi, Gutierrez, Harding, and Bagheri teach the collecting and analysis of data in order to maximize the utilization of resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Referring to Claim 15, see discussion of claim 13 above, while Rossi in view of Gutierrez in further view of Harding teaches the method above, Rossi further discloses a method having the limitations of:
Gutierrez teaches determining an entity that the node represents based upon metadata for the node, wherein the topic is identified based upon the determined entity (see; par. [0074] of Gutierrez teaches the comparison of unique identifiers that include metadata, par. [0100] where each node comprises metadata, and par. [0324] nodes represent topics are identified and analyzed).
The Examiner notes that Rossi teaches similar to the instant application analysis system receiving network data in the form of a temporal graph. Specifically, Rossi discloses the analysis system determines embeddings for the nodes in the temporal graph belonging to the same entity and it is therefore viewed as analogous art in the same field of endeavor. Additionally, Gutierrez teaches dynamic presentation of searchable contextual actions and data of users in a computing environment utilizing nodes as representation as it is comparable in certain respects to Rossi which analysis system receiving network data in the form of a temporal graph as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Rossi discloses the determining a community of users with similar temporal behavior and generate electrotonic content in the form of subgraphs with nodes and edges However, Rossi fails to disclose determining an entity that the node represents based upon metadata for the node, wherein the topic is identified based upon the determined entity.
Gutierrez discloses determining an entity that the node represents based upon metadata for the node, wherein the topic is identified based upon the determined entity.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Rossi determining an entity that the node represents based upon metadata for the node, wherein the topic is identified based upon the determined entity as taught by Gutierrez since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Rossi, and Gutierrez teach the collecting and analysis of data in order to maximize the utilization of resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Rossi in view of Gutierrez in further view of Harding does not explicitly disclose the following limitation, however,
Bagheri teaches identifying the topic of interest to the user comprises: identifying a node in at least one of the first subgraph or the second subgraph based upon a number of incoming edges to the node (see; par. [0093]-[0094] of Bagheri teaches determining similarity between nodes and edges for particular subgraphs).
The Examiner notes that Rossi teaches similar to the instant application analysis system receiving network data in the form of a temporal graph. Specifically, Rossi discloses the analysis system determines embeddings for the nodes in the temporal graph belonging to the same entity and it is therefore viewed as analogous art in the same field of endeavor. Additionally, Gutierrez teaches dynamic presentation of searchable contextual actions and data of users in a computing environment utilizing nodes as representation as it is comparable in certain respects to Rossi which analysis system receiving network data in the form of a temporal graph as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Harding teaches a clinical activity network to analyze data from sources including a customer database as it is comparable in certain respects to Rossi and Gutierrez which analysis system receiving network data in the form of a temporal graph as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Bagheri teaches a temporal identification of latent user communities using electronic content as it is comparable in certain respects to Rossi, Gutierrez, and Harding which analysis system receiving network data in the form of a temporal graph as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Rossi, Gutierrez, and Harding discloses the analysis system receiving network data in the form of a temporal graph. However, Rossi, Gutierrez, and Harding fails to disclose identifying the topic of interest to the user comprises: identifying a node in at least one of the first subgraph or the second subgraph based upon a number of incoming edges to the node.
Bagheri discloses identifying the topic of interest to the user comprises: identifying a node in at least one of the first subgraph or the second subgraph based upon a number of incoming edges to the node.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Rossi, Gutierrez, and Harding identifying the topic of interest to the user comprises: identifying a node in at least one of the first subgraph or the second subgraph based upon a number of incoming edges to the node as taught by Bagheri since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Rossi, Gutierrez, Harding, and Bagheri teach the collecting and analysis of data in order to maximize the utilization of resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Referring to Claim 16, see discussion of claim 13 above, while Rossi in view of Gutierrez in further view of Harding teaches the system above, Rossi in view of Gutierrez in further view of Harding does not explicitly disclose the following limitations, however,
Bagheri teaches generating a recommendation for the user based upon the third amount of time that the user is predicted to spend working on the topic during the future time period, wherein the recommendation indicates a suggested change in behavior of the user during the future time period (see; par. [0118] of Bagheri teaches providing a recommendation based on topics, par. [0142] where this recommendation is based on the changes in a user’s behavior), and
causing the recommendation to be presented within the GUI to the user (see; par. [0067] of Bagheri teaches a user interface (i.e. GUI), par. [0118] that provides a recommendation of presented topics).
The Examiner notes that Rossi teaches similar to the instant application analysis system receiving network data in the form of a temporal graph. Specifically, Rossi discloses the analysis system determines embeddings for the nodes in the temporal graph belonging to the same entity and it is therefore viewed as analogous art in the same field of endeavor. Additionally, Gutierrez teaches dynamic presentation of searchable contextual actions and data of users in a computing environment utilizing nodes as representation as it is comparable in certain respects to Rossi which analysis system receiving network data in the form of a temporal graph as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Harding teaches a clinical activity network to analyze data from sources including a customer database as it is comparable in certain respects to Rossi and Gutierrez which analysis system receiving network data in the form of a temporal graph as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Bagheri teaches a temporal identification of latent user communities using electronic content as it is comparable in certain respects to Rossi, Gutierrez, and Harding which analysis system receiving network data in the form of a temporal graph as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Rossi, Gutierrez, and Harding discloses the analysis system receiving network data in the form of a temporal graph. However, Rossi, Gutierrez, and Harding fails to disclose generating a recommendation for the user based upon the third amount of time that the user is predicted to spend working on the topic during the future time period, wherein the recommendation indicates a suggested change in behavior of the user during the future time period, and causing the recommendation to be presented within the GUI to the user.
Bagheri discloses generating a recommendation for the user based upon the third amount of time that the user is predicted to spend working on the topic during the future time period, wherein the recommendation indicates a suggested change in behavior of the user during the future time period, and causing the recommendation to be presented within the GUI to the user.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Rossi, Gutierrez, and Harding generating a recommendation for the user based upon the third amount of time that the user is predicted to spend working on the topic during the future time period, wherein the recommendation indicates a suggested change in behavior of the user during the future time period, and causing the recommendation to be presented within the GUI to the user as taught by Bagheri since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Rossi, Gutierrez, Harding, and Bagheri teach the collecting and analysis of data in order to maximize the utilization of resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Referring to Claim 17, see discussion of claim 1 above, while Rossi in view of Gutierrez in further view of Harding teaches the system above, Rossi in view of Gutierrez in further view of Harding does not explicitly disclose the following limitations, however,
Bagheri teaches the recommendation is additionally based upon a role of the user within an organization (see; par. [0125] of Bagheri teaches taking into account different rolls, par. [0118] that is used to provide recommendations).
The Examiner notes that Rossi teaches similar to the instant application analysis system receiving network data in the form of a temporal graph. Specifically, Rossi discloses the analysis system determines embeddings for the nodes in the temporal graph belonging to the same entity and it is therefore viewed as analogous art in the same field of endeavor. Additionally, Gutierrez teaches dynamic presentation of searchable contextual actions and data of users in a computing environment utilizing nodes as representation as it is comparable in certain respects to Rossi which analysis system receiving network data in the form of a temporal graph as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Harding teaches a clinical activity network to analyze data from sources including a customer database as it is comparable in certain respects to Rossi and Gutierrez which analysis system receiving network data in the form of a temporal graph as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Bagheri teaches a temporal identification of latent user communities using electronic content as it is comparable in certain respects to Rossi, Gutierrez, and Harding which analysis system receiving network data in the form of a temporal graph as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Rossi, Gutierrez, and Harding discloses the analysis system receiving network data in the form of a temporal graph. However, Rossi, Gutierrez, and Harding fails to disclose the recommendation is additionally based upon a role of the user within an organization.
Bagheri discloses the recommendation is additionally based upon a role of the user within an organization.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Rossi, Gutierrez, and Harding the recommendation is additionally based upon a role of the user within an organization as taught by Bagheri since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Rossi, Gutierrez, Harding, and Bagheri teach the collecting and analysis of data in order to maximize the utilization of resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Referring to Claim 19, see discussion of claim 18 above, while Rossi in view of Gutierrez in further view of Harding teaches the non-transitory computer readable storage medium above, Rossi further discloses a non-transitory computer readable storage medium having the limitations of:
identifying a second user that is associated with the work area based upon at least one of the first data included in the first subgraph or the second data included in the second subgraph (see; par. [0055] of Rossi teaches a selection of initial edges from recent edges, par. [0052] where the time associated with the edges have temporal values (i.e. time period), Abstract and par. [0016] of Rossi teaches a time value associated with edges and nodes including interaction time (i.e. time period) on a temporal specific graph (i.e. first subset)).
determining a role of the second user within the organization based upon at least one of the first data included in the first subgraph or the second data included in the second subgraph (see; Abstract and par. [0016] of Rossi teaches a time value associated with edges and nodes including interaction time (i.e. time period) on a temporal specific graph (i.e. first subset), par. [0034]-[0036] where platform activity data temporal graph including nodes, where the online platform activity representing nodes and edges along with timestamp).
Rossi in view of Guitierrez in further view of Harding does not explicitly disclose the following limitations, however,
Bagheri teaches comparing the third amount of time to a threshold amount of time, wherein the threshold amount of time is based upon a role of the user within an organization (see; par. [0121] of Bagheri teaches comparing temporal data to a threshold correlated with the actions of a user, par. [0125] taking into account the role of the user in the communities), and
when the third amount of time exceeds the threshold amount of time (see; par. [0121] of Bagheri teaches comparing temporal data to a threshold correlated with the actions of a user performing a cumulative occurrence frequency), and
causing a recommendation to be displayed to the user, wherein the recommendation indicates that the user delegate certain activities in the work area to the second user during the future time period (see; par. [0067] of Bagheri teaches a user interface (i.e. GUI), par. [0118] that provides a recommendation of presented topics), and
recommendation is based upon the role of the user within the organization and the role of the second user within the organization (see; par. [0125] of Bagheri teaches taking into account different rolls in an organization, par. [0118] that is used to provide recommendations), and
identifying, a topic of interest to the user based upon first data included in the first subgraph and second data included in the second subgraph (see; par. [0017] of Bagheri teaches identifying different topics regarding the user the user which is done by extracting multiple subgroups to identify multiple topics), and
determining, a first amount of time the user spent on activities for the topic of interest during the first time period and a second amount of time the user spent on activities for the topic of interest during the second time period (see; par. [0015]-[0017] of Bagheri teaches determining topics for the time periods, par. [0051]-[0055] where the topics that are determined are topics during different identified time periods), and
determining, a third amount of time that the user is predicted to spend working on the topic during a future time period based upon the first amount of time and the second amount of time the user spent on the topic (see; par. [0118] of Bagheri teaches determining during a future time (i.e. third time) interests in the future interests of the user (i.e. work), par. [0015]-[0017] where additional topics and content are determined for the designated or different time period).
The Examiner notes that Rossi teaches similar to the instant application analysis system receiving network data in the form of a temporal graph. Specifically, Rossi discloses the analysis system determines embeddings for the nodes in the temporal graph belonging to the same entity and it is therefore viewed as analogous art in the same field of endeavor. Additionally, Gutierrez teaches dynamic presentation of searchable contextual actions and data of users in a computing environment utilizing nodes as representation as it is comparable in certain respects to Rossi which analysis system receiving network data in the form of a temporal graph as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Harding teaches a clinical activity network to analyze data from sources including a customer database as it is comparable in certain respects to Rossi and Gutierrez which analysis system receiving network data in the form of a temporal graph as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Bagheri teaches a temporal identification of latent user communities using electronic content as it is comparable in certain respects to Rossi, Gutierrez, and Harding which analysis system receiving network data in the form of a temporal graph as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Rossi, Gutierrez, and Harding discloses the analysis system receiving network data in the form of a temporal graph. However, Rossi, Gutierrez, and Harding fails to disclose comparing the third amount of time to a threshold amount of time, wherein the threshold amount of time is based upon a role of the user within an organization, when the third amount of time exceeds the threshold amount of time, causing a recommendation to be displayed to the user, wherein the recommendation indicates that the user delegate certain activities in the work area to the second user during the future time period, recommendation is based upon the role of the user within the organization and the role of the second user within the organization, identifying, a topic of interest to the user based upon first data included in the first subgraph and second data included in the second subgraph, determining, a first amount of time the user spent on activities for the topic of interest during the first time period and a second amount of time the user spent on activities for the topic of interest during the second time period, and determining, a third amount of time that the user is predicted to spend working on the topic during a future time period based upon the first amount of time and the second amount of time the user spent on the topic.
Bagheri discloses comparing the third amount of time to a threshold amount of time, wherein the threshold amount of time is based upon a role of the user within an organization, when the third amount of time exceeds the threshold amount of time, causing a recommendation to be displayed to the user, wherein the recommendation indicates that the user delegate certain activities in the work area to the second user during the future time period, recommendation is based upon the role of the user within the organization and the role of the second user within the organization, identifying, a topic of interest to the user based upon first data included in the first subgraph and second data included in the second subgraph, determining, a first amount of time the user spent on activities for the topic of interest during the first time period and a second amount of time the user spent on activities for the topic of interest during the second time period, and determining, a third amount of time that the user is predicted to spend working on the topic during a future time period based upon the first amount of time and the second amount of time the user spent on the topic.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Rossi, Gutierrez, and Harding comparing the third amount of time to a threshold amount of time, wherein the threshold amount of time is based upon a role of the user within an organization, when the third amount of time exceeds the threshold amount of time, causing a recommendation to be displayed to the user, wherein the recommendation indicates that the user delegate certain activities in the work area to the second user during the future time period, recommendation is based upon the role of the user within the organization and the role of the second user within the organization, identifying, a topic of interest to the user based upon first data included in the first subgraph and second data included in the second subgraph, determining, a first amount of time the user spent on activities for the topic of interest during the first time period and a second amount of time the user spent on activities for the topic of interest during the second time period, and determining, a third amount of time that the user is predicted to spend working on the topic during a future time period based upon the first amount of time and the second amount of time the user spent on the topic as taught by Bagheri since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Rossi, Gutierrez, Harding, and Bagheri teach the collecting and analysis of data in order to maximize the utilization of resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Referring to Claim 20, see discussion of claim 18 above, while Rossi in view of Gutierrez in further view of Harding teaches the system above, Rossi in view of Gutierrez in further view of Harding does not explicitly disclose the following limitations, however,
Bagheri teaches generating an email that includes the identifier for the topic and the indication of the third amount of time (see; par. [0114]-[0119] of Bagheri teaches generating a email), and
transmitting the email to an email account of the user, wherein the email is presented on a display to the user (see; par. [0119] of Bagheri teaches transmitting an email, and par. [0070] displaying the information).
The Examiner notes that Rossi teaches similar to the instant application analysis system receiving network data in the form of a temporal graph. Specifically, Rossi discloses the analysis system determines embeddings for the nodes in the temporal graph belonging to the same entity and it is therefore viewed as analogous art in the same field of endeavor. Additionally, Gutierrez teaches dynamic presentation of searchable contextual actions and data of users in a computing environment utilizing nodes as representation as it is comparable in certain respects to Rossi which analysis system receiving network data in the form of a temporal graph as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Harding teaches a clinical activity network to analyze data from sources including a customer database as it is comparable in certain respects to Rossi and Gutierrez which analysis system receiving network data in the form of a temporal graph as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Bagheri teaches a temporal identification of latent user communities using electronic content as it is comparable in certain respects to Rossi, Gutierrez, and Harding which analysis system receiving network data in the form of a temporal graph as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Rossi, Gutierrez, and Harding discloses the analysis system receiving network data in the form of a temporal graph. However, Rossi, Gutierrez, and Harding fails to disclose generating an email that includes the identifier for the topic and the indication of the third amount of time, and transmitting the email to an email account of the user, wherein the email is presented on a display to the user.
Bagheri discloses generating an email that includes the identifier for the topic and the indication of the third amount of time, and transmitting the email to an email account of the user, wherein the email is presented on a display to the user.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Rossi, Gutierrez, and Harding generating an email that includes the identifier for the topic and the indication of the third amount of time, and transmitting the email to an email account of the user, wherein the email is presented on a display to the user as taught by Bagheri since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Rossi, Gutierrez, Harding, and Bagheri teach the collecting and analysis of data in order to maximize the utilization of resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bostick et al. (U.S. Patent 11,442,977 B2) discloses augmenting search queries based on personalized association patterns.
VANGALA et al. (U.S. Patent Publication 2022/0284402 A1) discloses an artificial intelligence driven personalization for electronic meeting creation and follow up.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to STEPHEN S SWARTZ whose telephone number is (571)270-7789. The examiner can normally be reached on Mon-Fri 9:00 - 6:00.
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/SSS/
Patent Examiner, Art Unit 3623
/DYLAN C WHITE/Primary Examiner, Art Unit 3625
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