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 Final Office Action is responsive to Applicant's amendment filed on 25 July 2025. Applicant’s amendment on 25 July 2025 amended Claims 1, 13, and 18. Currently Claims 1-20 are pending and have been examined. The Examiner notes that the 101 rejection has been maintained.
Response to Arguments
Applicant's arguments filed 25 July 2025 have been fully considered but they are not persuasive.
The Applicant argues on pages 10-11 that “Applicant respectfully disagrees with this characterization of the claimed… the alleged abstract idea identified by the Office bears no resemblance to any of the enumerated groupings of abstract ideas identified in the MPEP… such a characterization improperly reads out many of the important technical features of the claims which relate to extracting topic specific areas of a user graph related to user activity across a plurality of computer implemented applications and applying predictive intelligence by way of a machine learning model…”.
The Examiner respectfully disagrees.
In response to the arguments the Examiner notes that the applicant’s argument fails because it fundamentally mischaracterizes how the USPTO identifies abstract ideas under the current framework. The USPTO is not required to find an exact match to previously identified abstract ideas; rather, the Office evaluates whether the claim limitations falls within the three enumerated groupings of abstract ideas as set forth in MPEP 2106.04(a)(2): mathematical concepts, mental processes, and certain methods of organizing human activity.
The claims clearly recite abstract ideas from multiple groupings, even though in the instance of the rejection the Examiner pointed to one, but for the purpose of this response the full analysis will be provided. First, the claims recite mathematical concepts through the machine learning engine’s operations: determining amounts of time spent on activities (mathematical calculations), predicting future time allocations based on past data (mathematical relationships and calculations), and analyzing graph structures with nodes and edges (mathematical relationships). The AI-SME Update specifically addresses machine learning operations and makes clear that simply invoking an ML engine does not avoid abstract idea characterization when the underlying operations are mathematical in nature.
Second, the claims recite mental processes. According to MPEP 2106.04(a)(2), mental processes include “concepts performed in the human mind (including an observation, evaluation, judgement, opinion).” The claimed activities of identifying topics of interest, determining time spent on activities, and predicting future time allocations are evaluations and judgements that can be practically performed in the human mind. A person could manually review their calendar, emails, and documents; identifying topics they work on; calculate time spent; and predict future time needs based on past patterns. The 2025 memorandum emphasizes that mental process are those that: can be performed in the human mind, or by a human using a pen and paper.” The fact that a computer automates these mental processes does not transform them into non-abstract concepts.
Third, the claims fall within certain methods of organizing human activity, specifically “managing personal behavior or relationships or interactions between people.” The claims are fundamentally directed to analyzing and predicting how a user allocates their work time across different topics and activities – this is quintessentially about managing personal work behavior and productivity planning. The specification itself describes the invention’s purpose as helping users “better plan workloads” and understand how projects should be prioritized,” which are classic examples of organizing human work activity.
The Applicant assertion that the Office “improperly reads out important technical features” demonstrates a misunderstanding of Step 2A Prong One Analysis. At this stage, the Office must identify whether the claims recites a judicial exception – meaning whether the claim sets forth or describes an abstract idea. The Applicant’s “important technical features” such as “extracting topic specific areas of a user graph” and “ applying predictive intelligence by way of a machine learning engine” are evaluated at Step 2A Prong two to determine whether they integrate the abstract idea into a practical application. These features are not ignored; they are properly evaluated in the subsequent analysis.
Furthermore, the 2025 memorandum specifically warns against the Applicant’s approach of characterizing claims at a high level of generality to avoid abstract idea identification. The memorandum states Examiners should “distinguish claims that recite an exception (which require further eligibility analysis) and claims that merely involve an exception.” There, the claims explicitly recite a mathematical calculation (determining amount of time predicting future events), mental evaluations (identifying topics of interest), and methods of organizing human activities (work time management). These are not activities the claims merely “involve” – they are what the claims fundamentally describe and require.
The Applicant’s reliance on terminology like “user graph,” “machine learning engine,” and “plurality of applications” does not avoid abstract idea characterization. As the AI-SME Updated Example 47 Claim 2 demonstrates, simply reciting that activities are performed “by a computer” or “using” an artificial neural network does not prevent a finding that the claim recites an abstract idea. The claims here similarly recite using a machine learning engine to perform what are fundamentally mathematical calculations and mental process evaluations, which fall squarely within the enumerated groupings regardless of whether they are performed manually or via computer automation. The rejection is therefore maintained.
The Applicant argues on pages 12-13 that “the claims recite elements incapable of performance in the Mind and are Therefore not abstract… claims to not recite mental process when they do not contain limitations that can practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations… the claims are directed towards a technical solution that is necessarily rooted in computer technology to overcome a problem specifically arising in a specific realm of computer implemented technology”.
The Examiner respectfully disagrees.
In response to the arguments the Examiner notes that the Applicant’s arguments misapplies the mental process analysis by conflating the scale and speed of data processing with whether claim limitations can practically be performed in the human mind. The 2025 memorandum and AI-SME Update make clear that the relevant question is not whether a human could perform the claimed steps as efficiently as a computer, but whether the claimed steps describe mental activities such as observations, evaluations, judgements, and opinions that are within the realm of human mental capability.
The claims recite identifying topics of interest based on user activity data, determining amounts of time spent on those topics, and predicting future time allocation based on historical patterns. These are viewed as mental process activities, observations and evaluations that humans routinely perform when analyzing their own work habits. A person can review their calendar entries, email correspondence, document editing history, and meeting attendance to identify topics they work on; calculate time spent on each topic by reviewing timestamps; and predict future time needs based on observed patterns from prior weeks or months. The fact that the claims automate these mental processes by collecting data from multiple computer applications and using a machine learning engine to perform data from multiple computer applications and using a machine learning engine to perform the analysis faster and at greater scale does not transform the fundamental nature of what is being claimed from mental processes into non-abstract subject matter.
The AI-SME Update specifically addresses this issue in Example 47, where Claim 2 was found to recite abstract ideas despite involving an artificial neural network processing data that would be impractical for a human to analyze manually at the same scale. The Update explains that claims recite mental processes when the limitations “may be practically performed in the human mind using observation, evaluation, judgement, and opinion” – not whether they can be performed at the same speed or volume as a computer. Similarly, the claims here recite evaluative steps (i.e. identifying topics of interest, determining time allocations, predicting future behavior) that are mentally processes regardless of the data volume involved.
Furthermore, the Applicant’s assertion that the claims are “necessarily rooted in computer technology to overcome a problem specifically arising in computer implemented technology” is a conclusory statement unsupported by the claim language itself. The problem being addressed is not a technical computer problem but rather a business productivity problem: helping users understand how they allocate their time and predict future workload. This is evident from the specification’s own description emphasizing improved work planning and resource allocation – these are not computer functionality problems but rather human organizational challenges. The 2025 memorandum warns against exactly this type of argument, noting that Examiners must evaluate “whether the claim invokes computers or other machinery merely as a tool to perform an existing process, or whether the claim purports to improve computer capabilities or to improve an existing.” Here, the claims invoke computer technology as a tool to automate the existing human process of analyzing work patters and predicting future time needs.
The MPEP 2106.04(a)(2) subsection III provides guidance on when claims do not recite mental processes: when limitations “cannot practically be performed in the human mind.” The examples given includes cases involving specific hardware implementations or transformations that are physically impossible for humans to perform mentally. The claims here contain no such limitations – they describe data gathering, pattern dentification, mathematical calculations, and predictions, that are all within the scope of human mental capabilities, merely automated and scaled up through computer implementation. The user graph structure with nodes and edges is simply a data organization method; identifying topics and predict time allocation represents automated statistical analysis – all activities that humans perform mentally when analyzing their own work patterns, albeit with less data and lower precision.
The Applicant has not identified any specific claim limitation that is physically or mentally impossible for a huma to perform. Instead, the argument relies on the volume of data processed and the speed of processing, which are characteristics of computer automation, not indicators that the claimed steps are beyond human mental capability. According to the guidance, this approach of arguing the scale of efficiency makes mental process non-abstract is explicitly rejected by the USPTO framework. The rejection is therefore maintained.
The Applicant argues on pages 13-14 that “the independent claims recite elements that integrate any alleged abstract idea recite in such claims into a practical application of the abstract idea… to ascertain whether the claimed invention improves the functioning of a computer or improves another technology or technical field, the Examiner is to evaluate the specification to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize”.
The Examiner respectfully disagrees.
In response to the arguments the Examiner notes that the Applicant’s arguments fails because it merely asserts that the claims integrate the abstract idea into a practical application without identifying specific claim limitations that accomplish this integration, and the specification lacks sufficient technical detail to demonstrate any improvement to computer functionality or technology. The 2025 memorandum makes clear that while the specification should be evaluated to determine if it describes an improvement, “ the claim must be evaluated to ensure that the claim itself reflects he disclosed improvement.” Here, even assuming the specification describes some benefit, the claims as drafted do not reflect any specific technological improvement. Specifically the 2025 memorandum requires “the claim must be evaluated to ensure the claim itself reflects the disclosed improvement.” The claims recite only generic computer components, a “user graph”, “machine learning engine”, “plurality of applications,” and display functions – described at such a high level that they amount to “mere instructions to implement an abstract idea on a computer” – under MPEP 2106.05(f). The claims provide no technical details about how the machine learning engine operates, what algorithm it employs, or what specific improvements it provides to computer functionality.
The AI-SME Update Example 47 Claim 2 directly addresses this issue, finding a claim ineligible despite using an artificial neural network (ANN) because “ the ANN is used to generally apply the abstract idea without how the trained ANN functions. The ANN is described at a high level such that it amounts to using a computer with a generic ANN to apply the abstract idea.” The claims here suffer the identical deficiency. The 2025 memorandum emphasizes that eligible claims must demonstrate “ a practical solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome.” These claims recite only the outcome – predicting future time allocation – without any particular technical means that would represent a computer technology improvement.
Comparing to eligible examples, Example 47 Claim 3 was found to be eligible because it included specific technical steps: “detecting a source address… in real-time; dropping the one or more malicious network packets in real-time; and blocking future traffic from the source address.” These concrete actions demonstrated how network security functionality was improved. The claims here lack any comparable specificity – they invoke generic machine learning for generic predictions and generic display, which are conventional computer operations. The user graph is standard data organization, selecting subgraphs is routine filtering, and using ML for pattern prediction is well-understood technique. Under the memorandum there-factor test: (1) claims recite ideas of solutions rather than particular solutions; (2) computers are tools performing existing process rather than improved capabilities; and (3) the application is general rather than particular. The claims therefore do not integrate the abstract idea into a practical application and therefore the rejection is maintained.
The Applicant argues on pages 14-15 that “one of ordinary skill in the art would recognize the claimed invention as providing an improvement over conventional technologies… Applicant has amended claim 1 to specifically clarify aspects related to obtaining user activity data and generating a corresponding user graph based upon the user activity data… claim 1 are directed towards improvements over conventional technologies and are realized as improvement to the computer technology itself… existing technologies failed to provide reliable tracking or predictive intelligence across a plurality of applications”.
The Examiner respectfully disagrees.
In response to the arguments the Examiner notes that the Applicant’s assertion that the claims improve the computer technology is contradicted by the claim language itself, which describes improvement to business productivity tracking, not computer functionality. The 2025 memorandum instructs Examiner’s to evaluate “whether the claim invokes computers or other machinery merely as a tool to perform an existing process, or whether the claim purports to improve computer capabilities or to improve an existing technology.” The claims here automate the existing human process of tracking work activities and predicting future time allocation – activities humans have performed manually using calendars, time logs, and personal observation for decades.
The Applicant’s statement that “existing technologies failed to provide reliable tracking or predictive intelligence across a plurality of applications” describes a data aggregation problem, not a computer technology problem. Aggregate data from multiple sources and applying statistical analysis to make predictions are well-understood, routine computer operations.. The AI-SME Update makes it clear that simply applying conventional computer techniques to a new data source does not constitute a technological improvement. Example 47 Claim 2 was ineligible despite using backpropagation and gradient decent algorithms because these were described generically without technical details showing how computer functionality was improved.
The amendments adding “obtaining user activity data and generating a corresponding user graph” do not demonstrate technological improvement – merely describe conventional data collection and organization using standard graph database structures. The 2025 Memorandum emphasizes that improvements must show “the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome” with specific technical constraints. The claims provide no particular machine learning architecture, no specific graph generation algorithms, no novel data processing techniques, and no technical details demonstrating how computer processing memory usage, or computational efficiency is improved.
Moreover, the specification’s own description reveals the invention’s true nature: it helps users “better plan workloads” and understand how projects should be prioritized” – these are business benefits, not technological improvements. Under MPEP 2016.05(a), claims must improve “the functioning of a computer, or to another technology or technical field.” Enabling better business decisions through data aggregation and prediction does not improve how computers function; it uses computers as tools to provide business intelligence, which falls under the ”apply it” consideration of MPEP 2016.05(f). The claims therefore fail to demonstrate integration into a practical application through technological improvement, and therefore the rejection is maintained.
The Applicant argues on pages 15-16 that “the elements of the claims amount to significantly more than the abstract idea… the claimed subject matter is patentably distinct over the references of record, evidence that the claimed subject matter is patentably distinct over the references of record, evidence that the claimed subject matter is novel and not directed towards well-understood, routine, and conventional activities previously known in the industry… because the claims are not suggested by the prior art, it follows that the elements of the claim are not well-understood, routine, or conventional and therefore amount to significantly more”.
The Examiner respectfully disagrees.
In response to the arguments the Examiner notes that as indicated above the Applicant fundamentally misunderstands the relationship between novelty under 35 U.S.C. 102/103 and the “significantly more” analysis under Step 2B of the Alice/Mayo framework. The Supreme Court in Mayo v. Prometheus explicitly rejected this argument, holding the patent eligibility and novelty are separate inquiries. The 2025 memorandum confirms that Step 2B evaluates whether claim elements are “well-understood, routine, and conventional” in the field at the timing of filing, which is distinct from whether the particular combination is novel. MPEP 2016.05(d) makes clear that “a claim directed to a new abstract idea is still an abstract idea” and remains ineligible absent an inventive concept.
The well-understood, routine, and conventional analysis under step 2B examines whether individual claim elements or their combination represent more than routine application of abstract ideas using conventional technology. Here, the claim elements consist of: (1) collecting user activity data from multiple applications – conventional data aggregation; (2) organizing data in graph structures with nodes and edges – standard database techniques; (3) filtering data by timestamps – routine data processing; (4) applying machine learning to identify patterns and make predictions – generic application of well-known ML techniques; and (5) displaying results – conventional output. Each element represents well-understood computer operations that were routine in the industry at the time of filing.
The AI-SME Update Example 47 Claim 2 directly addresses this issue. That claim was found ineligible at Step 2B even though it recited specific algorithms (backpropagation and gradient descent) because these were “well-understood, routine, conventional activity” described at a high levels of generality. The Update states: “receiving and outputting were considered insignificant extra solution activity… The limitations are mere data gathering and output recited at a high level of generality and amount to receiving or transmitting data over a network, which is well-understood, routine, conventional activity.” The claims here similarly recite data gathering (obtaining user activity data), conventional processing (graph generation, ML analysis), and output (displaying predictions) – all described generically without technical specifics that would demonstrate an inventive concept.
Furthermore, novelty of the application domain (applying known techniques to workplace productivity tracking) does not establish that the claim elements themselves are non-conventional. Under MPEP 2016.05(h), “generally linking the use of the judicial exception to a particular technological environment or field of use” is insufficient. The fact that prior art may not have combined these specific data sources for this particular business purpose does not mean the technological components – graph database, machine learning prediction engines, timestamp filtering, and display interfaces – represent anything other than conventional computer implementation. The Applicant’s argument would improperly conflate the separate statutory requirements of eligibility and patentability, effectively eliminating the Alice/Mayo framework whenever claims recite novel combinations of abstract ideas implemented on generic computers. The rejection is therefore maintained.
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 the abstract idea of a time tracking which tracks personal productivity information and identify potential improvements. The claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more than the judicial exception itself. Claim(s) (1-20) is/are directed to an abstract idea without significantly more.
Step 1
Regarding Step 1 of the Subject Matter Eligibility Test for Products and Processes (from the January 2019 §101 Examination Guidelines), claim(s) (1-12) is/are directed to a system, claim(s) (13-17) is/ are directed to a method, and claims(s) (18-20) is/are directed to a non-transitory computer readable storage medium and therefore the claims recites a series of steps and, therefore the claims are viewed as falling in statutory categories.
Step 2A Prong 1
The claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) a mental process. Specifically, the independent claims 1, 13, and 18 recite a mental process: as drafted, the claim recites the limitation of determining an amount of time spent by users on activities and predicting activities that will be worked on which is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting a processor, nothing in the claim precludes the determining step from practically being performed in the human mind. For example, but for the processor language, the claim encompasses the user manually analyzing a user’s interest based on activities to predict future activities. The mere nominal recitation of a generic processor does not take the claim limitation out of the mental processes grouping. This limitation is a mental process. While the Guidance provides that claims do not recite a mental process when they contain limitations that can practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations (GPS position calculation, network monitoring, data encryption for communication, rendering images. However with regard to the instant application the Examiner has reviewed the disclosure and determined that the underlying claimed invention is described as a concept that is performed in the human mind and/or with the aid of a pen and paper, and thus it is viewed that the applicant is merely claiming that concept performed 1) on a generic computer, 2) in a computer environment or 3) is merely using a computer as a tool to perform the concept, and therefore is considered to recite a mental process.
Note to the Applicant per the 2019 October Guidance: The 2019 PEG sets forth a test that distills the relevant case law to aid in examination, and does not attempt to articulate each and every decision. As further explained in the 2019 PEG, the Office has shifted its approach from the case-comparison approach in determining whether a claim recites an abstract idea and instead uses enumerated groupings of abstract ideas. The enumerated groupings are firmly rooted in Supreme Court precedent as well as Federal Circuit decisions interpreting that precedent. By grouping the abstract ideas, the 2019 PEG shifts examiners’ focus from relying on individual cases to generally applying the wide body of case law spanning all technologies and claim types. In sum, the 2019 PEG synthesizes the holdings of various court decisions to facilitate examination.
Step 2A Prong 2
Specifically, the determined judicial exception is not integrated into a practical application because the generically recited computer elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer and additionally the data obtaining, generating, identifying and displaying steps required to use the determining steps do not add a meaningful limitation to the method as they are insignificant extra-solution activity (including post solution activity).
The claim recites the additional element(s): that a processor is used to perform the determining steps. The processor in both steps is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of processing data (time tracking applications which tracks personal productivity information and identify potential improvements). This generic processor limitation is no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to the abstract idea.
The claim recites the additional element(s): obtaining user activity, generating a user graph, identifying a first subgraph, identifying a second subgraph, identifying a topic of interest, and displaying topics performs the determining steps. The obtaining, generating, identifying, and displaying steps are recited at a high level of generality (i.e., as a general means of obtaining, generating, identifying and displaying data for use in the determining steps), and amounts to mere manipulation, which is a form of insignificant extra-solution activity. The processor that performs the displaying steps are also recited at a high level of generality, and merely automates the determining steps. Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer component (the processor).
The Examiner has further determined that the claims as a whole does not integrate a judicial exception into a practical application in order to provide an improvement in the functioning of a computer or an improvement to other technology or technical field. It has been determined that based on the disclosure does not provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. It has not been provided clearly in the disclosure that the alleged improvement would be apparent to one of ordinary skill in the art, but is instead in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art, and therefore does not improve the technology. Second, in the instance, which in this case it is not clear that the specification sets forth an improvement in technology, the claim must not reflect the disclosed improvement (the claims must include components or steps of the invention that provide the improvement described in the specification).
Note to the Applicant from the October 2019 Guidance: Generally, examiners are not expected to make a qualitative judgment on the merits of the asserted improvement. If the examiner concludes the disclosed invention does not improve technology, the burden shifts to applicant to provide persuasive arguments supported by any necessary evidence to demonstrate that one of ordinary skill in the art would understand that the disclosed invention improves technology. Any such evidence submitted under 37 C.F.R. § 1.132 must establish what the specification would convey to one of ordinary skill in the art and cannot be used to supplement the specification. For example, in response to a rejection under 35 U.S.C. § 101, an applicant could submit a declaration under § 1.132 providing testimony on how one of ordinary skill in the art would interpret the disclosed invention as improving technology and the underlying factual basis for that conclusion.
For further clarification the Examiner points out that the claim(s) 1-20 recite(s) obtaining user activity data, generating a user graph, identifying a first subgraph, identifying a second subgraph, identifying a topic of interest, determining a first amount of time, determining a third amount of time, and displaying a topic which are viewed as an abstract idea in the form of a mental process. This judicial exception is not integrated into a practical application because the use of a computer obtaining, generating, identifying, determining, and displaying which is the abstract idea steps of valuing an idea (time tracking applications which tracks personal productivity information and identify potential improvements) in the manner of “apply it”.
Thus, the claims recites an abstract idea directed to a mental process (i.e. time tracking which tracks personal productivity information and identify potential improvements). Using a computer to obtaining, generating, identifying, and determining the data resulting from this kind of mathematically-based, mental process merely implements the abstract idea in the manner of “apply it” and does not provide 'something more' to make the claimed invention patent eligible. The claimed limitations of a computing device is not constraining the abstract idea to a particular technological environment and do not provide significantly more.
The time tracking program which tracks personal productivity information and identify potential improvements would clearly be to a mental activity that a company would go through in order to predict work in a future time period. The specification makes it clear that the claimed invention is directed to the mental activity data gathering and data analysis to determine how to determine future activities of a user:
The dependent claims recite elements that narrow the metes and bounds of the abstract idea but do not provide ‘something more’.
The dependent claims do not remedy these deficiencies.
Claims 26, 14, 15, and 19 recite limitations which further limit the claimed analysis of data.
Claims 2-5, 7, 10, 16, and 20 recites limitations directed to claim language viewed insignificantly extra solution activity.
Using a computer to perform the data processing as claimed is merely implementing the abstract idea in the manner of “apply it” and does not provide significantly more. Additionally with respect to the Berkheimer the Examiner points out that the steps of the claim are viewed to be to nothing more than spell out what it means to apply it on a computer and cannot confer patent-eligibility as there are no additional limitations beyond applying an abstract idea, restricted to a computer. As the claims are merely implementing the abstract idea in the manner of “Apply It” the need for a Berkheimer analysis does not apply and is not required. With respect to the currently filed claims the implementing steps can be found in Bagheri which discloses how the claims alone and in combination are viewed to be well understood, routine and conventional based on point 3 of the Berkheimer memo and subsequent evidence, complying with and providing evidence.
Claims 8, 9, 11, 12, and 17 recites limitations directed to claim language viewed non-functional data labels.
Thus, the problem the claimed invention is directed to answering the question based on time tracking which tracks personal productivity information and identify potential improvements. This is not a technical or technological problem but is rather in the realm of resource management and therefore an abstract idea.
Step 2B
The claim(s) 1-20 does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed with respect to Step 2A Prong Two, the additional element in the claim amounts to no more than mere instructions to apply the exception using a generic computer component.
The same analysis applies here in 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. This is the case because in order for the claims to be viewed as significantly more the claims must incorporate the integral use of a machine to achieve performance of a method, in contrast to where the machine is merely an object on which the method operates, which does not provide significantly more in order for a machine to add significantly more, it must play a significant part in permitting the claimed method to be performed, rather than function solely as an obvious mechanism for permitting a solution to be achieved more quickly. Whether its involvement is extra-solution activity or a field-of-use, i.e., the extent to which (or how) the machine or apparatus imposes meaningful limits on the claim. Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not provide significantly more. Additionally, another consideration when determining whether a claim recites significantly more is whether the claim effects a transformation or reduction of a particular article to a different state or thing. "[T]ransformation and reduction of an article ‘to a different state or thing’ is the clue to patentability of a process claim that does not include particular machines. All together the above analysis shows there is not improvement in computer functionality, or improvement to any other technology or technical field. The claim is ineligible.
With respect to the Berkheimer as noted above the same analysis applies to the 2B where the claims are viewed as applying it and as such no further analysis is required. However, with respect to the claims that are viewed as extra solution or post solution activity the Examiner notes that the claims are viewed as well-understood, routine, and conventional because a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s). An appropriate publication could include a book, manual, review article, or other source that describes the state of the art and discusses what is well-known and in common use in the relevant industry.
The dependent claims recite elements that narrow the metes and bounds of the abstract idea but do not provide ‘something more’. Specifically, the dependent claims do not remedy these deficiencies of the independent claims.
With respect to the legal concept of prima facie case being a procedural tool of patent examination, which allocates the burdens going forward between the examiner and the applicant. MPEP § 2106.07 discusses the requirements of a prima facie case of ineligibility. In particular, the initial burden was on the Examiner and believed to be properly provided as to explain why the claim(s) are ineligible for patenting because of the above provided rejection which clearly and specifically points out in accordance with properly providing the requirement satisfying the initial burden of proof based on the Guidance from the United States Patent and Trademark Office and the burden now shifts to the applicant.
Therefore, based on the above analysis as conducted based on the Guidance from the United States Patent and Trademark Office the claims are viewed as a court recognized abstract idea, are viewed as a judicial exception, does not integrate the claims into a practical application, and does not provide an inventive concept, therefore the claims are ineligible.
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.
Claim 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bagheri et al. (U.S. Patent Publication 2018/0075147 A1) (hereafter Bagheri) in view of Gutierrez et al. (U.S. Patent Publication 2022/0309037 A1) (proper support is found in provisional 63/191,852) (hereafter Gutierrez).
Referring to Claim 1, A computing system, comprising:
a processor (see; par. [0060] of Bagheri teaches a processor).
memory storing instructions that, when executed by the processor, cause the processor to perform acts comprising (see; par. [0060] of Bagheri teaches a computer readable memory).
identifying a first subgraph of a user graph based on a set of first timestamps within the user graph (see; par. [0094]-[0096] of Bagheri identifying a subgraph of concepts for a first time, par. [0051] with generated time stamps).
wherein the first subgraph represents first activities performed by the user in the plurality of computer implemented applications during a first time period (see; par. [0103] of Bagheri teaches a designated subgraphs include nodes and edges for a designated time period, par. [0017] where data is extracted to create subgraphs that represent concepts (i.e. topics), par. [0049]-[0055] that identify topics and related concepts of the user).
identifying a second subgraph of the user graph based on a set of second timestamps within the user graph, wherein the second subgraph represents second activities performed by the user in the plurality of computer implemented applications during a second time period (see; par. [0103] of Bagheri teaches identifying a designated subgraph (i.e. second), par. [0051]-[0055] that identifies the timestamp for that subgraph that provides information on the topic during the identified time period (i.e. second)).
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).
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).
causing an identifier for the topic and an indication of the third amount of time to be displayed to the user (see; par. [0111] of Bagheri teaches a user topic viewed at a third time period of time).
Bagheri does not explicitly disclose the following limitation, 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 Bagheri teaches similar to the instant application teaches temporal identification of latent user communities using electronic content. Specifically, Bagheri discloses the determining a community of users with similar temporal behavior and generate electrotonic content in the form of subgraphs with nodes and edges 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 Bagheri which temporal identification of latent user communities using electronic content 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.
Bagheri 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, Bagheri 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 Bagheri 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, Bagheri, 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.
Bagheri 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 Bagheri teaches similar to the instant application teaches temporal identification of latent user communities using electronic content. Specifically, Bagheri discloses the determining a community of users with similar temporal behavior and generate electrotonic content in the form of subgraphs with nodes and edges 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 Bagheri which temporal identification of latent user communities using electronic content 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 Bagheri and Gutierrez which temporal identification of latent user communities using electronic content 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.
Bagheri and Gutierrez 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, Bagheri 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 Bagheri 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, Bagheri, 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 2, see discussion of claim 1 above, while Bagheri in view of Gutierrez in further view of Harding teaches the system above, Bagheri further discloses a system having the limitations of:
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).
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)).
Referring to Claim 3, see discussion of claim 2 above, while Bagheri in view of Gutierrez in further view of Harding teaches the system above, Bagheri further discloses a system having the limitations of:
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).
Referring to Claim 4, see discussion of claim 1 above, while Bagheri in view of Gutierrez in further view of Harding teaches the system above, Bagheri further discloses a system having the limitations of:
prior to identifying the first subgraph, selecting a first subset of edges in the user graph that have corresponding timestamps falling within the first time period (see; par. [0011] of Bagheri teaches identifying time periods associated with edges related to subsets of the users transient topics, par. [0017] for subgraphs providing edges and nodes).
generating the first subgraph based upon the first subset of edges, wherein the first subgraph comprises a first subset of nodes in the nodes, wherein the first subset of nodes are connected via the first subset of edges (see; (see; par. [0011] of Bagheri teaches identifying time periods associated with edges related to subsets of the users transient topics, par. [0017] for generating subgraphs providing edges and nodes).
prior to identifying the second subgraph, selecting a second subset of edges in the user graph that have corresponding timestamps falling within the second time period (see; par. [0115]-[0116] of Bagheri teaches analyzing edges to a specific time period in multiple time periods).
generating the second subgraph based upon the second subset of edges, wherein the second subgraph comprises a second subset of nodes in the nodes, wherein the second subset of nodes are connected via the second subset of edges (see; par. [0115]-[0118] of Bagheri teaches analyzing edges to a specific time period (i.e. second time period) in multiple time periods, par. [0017] for generating subgraphs providing edges and nodes that indicate connections to other nodes and edges).
Referring to Claim 5, see discussion of claim 1 above, while Bagheri in view of Gutierrez in further view of Harding teaches the system above, Bagheri further discloses a system having the limitations of:
each node in the user graph represents: an entity associated with the user; or an activity of the user (see; par. [0003]-[0007] of Bagheri teaches a graphs that depicts nodes in a graph that indicate topics from users).
Referring to Claim 6, see discussion of claim 1 above, while Bagheri in view of Gutierrez in further view of Harding teaches the system above, Bagheri further discloses a system having the limitations of:
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).
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).
obtaining search results for the search (see; par. [0131] of Bagheri teaches obtaining results of analysis requested by the user (i.e. search results)).
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)
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).
Referring to Claim 7, see discussion of claim 1 above, while Bagheri in view of Gutierrez in further view of Harding teaches the system above, Bagheri further discloses a system having the limitations of:
identifying, entities associated with the topic of interest during the first time period based upon the first data comprised by the first subgraph (see; par. [0007] and par. [0017] of Bagheri teaches identifying topics of a subgraph that provide par. [0080] the association with temporal topics (i.e. time period)).
causing identifiers for the entities to be displayed to the user (see; par. [0070] of Bagheri teaches displaying information in a visual format for the user).
Bagheri does not explicitly disclose the following limitation, however,
Gutierrez teaches via the ML engine (see par. [0086] of Gutierrez teaches the use of machine learning to identify nodes and activities associated with the nodes).
The Examiner notes that Bagheri teaches similar to the instant application teaches temporal identification of latent user communities using electronic content. Specifically, Bagheri discloses the determining a community of users with similar temporal behavior and generate electrotonic content in the form of subgraphs with nodes and edges 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 Bagheri which temporal identification of latent user communities using electronic content 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.
Bagheri 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, Bagheri fails to disclose via the ML engine.
Gutierrez discloses via the ML engine.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Bagheri via the ML engine 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, Bagheri, 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.
Referring to Claim 8, see discussion of claim 7 above, while Bagheri in view of Gutierrez in further view of Harding teaches the system above, Bagheri further discloses a system having the limitations of:
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).
Referring to Claim 9, see discussion of claim 1 above, while Bagheri in view of Gutierrez in further view of Harding teaches the system above, Bagheri further discloses a system having the limitations of:
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).
Referring to Claim 10, see discussion of claim 1 above, while Bagheri in view of Gutierrez in further view of Harding teaches the system above, Bagheri further discloses a system having the limitations of:
wherein each edge in the user graph comprises a timestamp, (see; par. [0017] of Bagheri teaches each edge in the graph is related to a timestamp of a concept).
the acts further comprising: 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).
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).
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)).
Referring to Claim 11, see discussion of claim 1 above, while Bagheri in view of Gutierrez in further view of Harding teaches the system above, Bagheri further discloses a system having the limitations of:
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).
Referring to Claim 12, see discussion of claim 1 above, while Bagheri in view of Gutierrez in further view of Harding teaches the system above, Bagheri further discloses a system having the limitations of:
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)).
Referring to Claim 13, Bagheri in view of Gutierrez in further view of Harding teaches method. Claim 12 recites the same or similar limitations as those addressed above in claim 1, Claim 12 is therefore rejected for the same reasons as set forth above in claim 1.
Referring to Claim 14, see discussion of claim 13 above, while Bagheri in view of Gutierrez in further view of Harding teaches the method above, Bagheri 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. [0017] of Bagheri teaches identifying different topics regarding the user the user which is done by extracting multiple subgroups to identify multiple topics).
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)).
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).
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,
Gutierrez teaches via the ML engine (see par. [0086] of Gutierrez teaches the use of machine learning to identify nodes and activities associated with the nodes).
The Examiner notes that Bagheri teaches similar to the instant application teaches temporal identification of latent user communities using electronic content. Specifically, Bagheri discloses the determining a community of users with similar temporal behavior and generate electrotonic content in the form of subgraphs with nodes and edges 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 Bagheri which temporal identification of latent user communities using electronic content 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.
Bagheri 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, Bagheri fails to disclose via the ML engine.
Gutierrez discloses via the ML engine.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Bagheri via the ML engine 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, Bagheri, 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.
Referring to Claim 15, see discussion of claim 13 above, while Bagheri in view of Gutierrez in further view of Harding teaches the method above, Bagheri further discloses a method having the limitations of:
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).
Bagheri does not explicitly disclose the following limitation, however,
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 Bagheri teaches similar to the instant application teaches temporal identification of latent user communities using electronic content. Specifically, Bagheri discloses the determining a community of users with similar temporal behavior and generate electrotonic content in the form of subgraphs with nodes and edges 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 Bagheri which temporal identification of latent user communities using electronic content 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.
Bagheri 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, Bagheri 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 Bagheri 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, Bagheri, 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.
Referring to Claim 16, see discussion of claim 13 above, while Bagheri in view of Gutierrez in further view of Harding teaches the method above, Bagheri further discloses a method having the limitations of:
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).
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).
Referring to Claim 17, see discussion of claim 16 above, while Bagheri in view of Gutierrez in further view of Harding teaches the method above, Bagheri further discloses a method having the limitations of:
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).
Referring to Claim 18, Bagheri 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.
Referring to Claim 19, see discussion of claim 18 above, while Bagheri in view of Gutierrez in further view of Harding teaches the non-transitory computer readable storage medium above, Bagheri further discloses a non-transitory computer readable storage medium having the limitations of:
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).
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).
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; Abstract of Bagheri teaches the determination of users as part of a community and their temporal contributions, par. [0094]-[0096] of Bagheri identifying a subgraph of concepts for a specific time, par. [0051] with generated time stamps).
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; 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, par. [0017] for generating subgraphs (i.e. first and second) providing edges and nodes that indicate connections to other nodes and edges).
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).
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).
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).
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).
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).
Referring to Claim 20, see discussion of claim 18 above, while Bagheri in view of Gutierrez in further view of Harding teaches the method above, Bagheri further discloses a method having the limitations of:
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).
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).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Stickler et al. (U.S. Patent 11,836,653 B2) discloses aggregating enterprise graph content around user-generated topics.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/SSS/
Patent Examiner, Art Unit 3623
/RUTAO WU/Supervisory Patent Examiner, Art Unit 3623
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