DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 1/02/2026 has been entered.
In response to Final Communications received 11/7/2025, Applicant, on 1/2/2026, amended Claims 1-4, 8-12, 15-18, and 20. Claims 1-20 are pending in this case, are considered in this application, and have been rejected below.
Response to Arguments
Arguments regarding 35 USC §101 Alice – The rejection has been removed in light of Applicant’s amendments. The claims are patent eligible as they meet the Alice test for eligibility under 35 USC §101 and the MPEP as the claims recite limitations which are not abstract under Prong 2 of step 2A of the Alice analysis, as any abstraction recited in the Claim limitations which may be construed as “A Mental Process” or “Certain Method of Organizing Human Activity” such as utilizing observations, evaluations, and judgments in the form of collecting, analyzing, and transmitting information for the purposes of generating a status report, are integrated into a practical application, as the additional elements applies the judicial exception in a meaningful way by training a neural network activity categorizer which is used to produce activity categories based on inputs received during operations of the activity categorizer, wherein during the training the neural network of the activity categorizer, the neural network is adjusted to produce desired outputs based on the inputs received during the operations of the activity categorizer and activity categories produced by the activity categorizer during the operations, which is a feedback loop which improves the neural network of the activity categorizer using an output from the categorizer, the output being the activity categories, along with the other limitations of the Claims. Thus, independent Claims 1, 10, and 16 are not directed at an abstract idea under 2A Prong 2 of the Alice Analysis of the MPEP, as they are integrated into a practical application and thus eligible under 35 USC §101 Alice.
Arguments regarding 35 USC §103 – Applicant asserts that the combination of Lassoued, Meyerzon, and Neumann does not teach the amended limitations of the claims, particularly the receiving and generating steps, stating that monitoring a health state of a user is not the same as monitoring activity. Examiner disagrees as Lassoued teaches receiving from a sensor, by an activity monitor, a detected user presence at a location as in [0023] where a sensor is used on a piece of monitoring equipment (activity monitor) to detect physical/virtual location of where the user while performing an activity, which is a presence of a user, generating activity data having information on an activity in which the detected user engages, including a time period during which the detected user was engaged in the activity, an activity location, and a description of the activity of detected user as in [0020] where activity data is received which has planned activities, environmental data, user activity, etc. and in [0023] the location data of the activity which is detected from a sensor, and teaches [0092] machine learning and use of a neural network with back propagation, along with using feedback to train the model as in [0088-0092]. Meyerzon teaches attributes of detected/monitored activities, jobs and tasks as in [0042], status reports being generated monthly as in [0044], and training of the learning model/component using received information as in [0096] as well as use of recurrent and trained neural networks as in [0072]. Neumann teaches a neural network which is adjusted based on the connections and weights which are received inputs, to produce desired values as in [0036]. This teaches the amended limitations of the Claims.
Therefore, the arguments are non-persuasive, the combination of Lassoued, Meyerzon, and Neumann teaches the amended limitations of the Claims, and the rejection of the Claims and their dependents are maintained under 35 USC 103.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-2, 4, 6, 8, 10-11, 13, 15-17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Lassoued (U.S. Publication No. 2020/036,7807) in view of Meyerzon (U.S. Publication No. 2020/040,1983) in further view of Neumann (U.S. Publication No. 2022/000,4915).
Regarding Claims 1, 10, and 16, Lassoued, a computing device for intelligent monitoring of a health state of a user engaged in operation of a computing device, teaches a computer program product for processing information on user activity to categorize and generate output, the computer program product comprising a computer readable storage medium having computer readable program code embodied therein that is executable to perform operations ([0061-63] computer program product with medium and modules to perform the limitations), the operations comprising:
receiving from a sensor, by an activity monitor, a detected user presence at a location ([0023] a sensor is used on a piece of monitoring equipment (activity monitor) to detect physical/virtual location of where the user is performing an activity);
generating activity data having information on an activity in which the detected user engages, including a time period during which the detected user was engaged in the activity, an activity location, and a description of the activity of detected user ([0020] activity data is received which has planned activities, environmental data, user activity, etc. and in [0023] the location data of the activity which is detected from a sensor as above);
processing, by an activity categorizer comprising a machine learning model classifier, the information on the time period, activity location, and the activity of detected user to output an activity category ([0106-107] activities are categorized by use of machine learning as in [0025] and [0029]);
Although Lassoued teaches generating, by a status report generator/modules as in ([0074] module/component blocks are used for software in the system), a report for the activity category, including the activity category of the activity and including the time period, the activity location, and the activity of detected user ([0101] reports are generated for the activities and the activities categories and these are in defined time periods as in [0073], and the activity location as in [0023] above); and saving to a database in in [0078], it does not explicitly state this is a status report, there are key attributes of the activity category.
Meyerzon, a system and method for extracting and surfacing user work attributes from data sources, teaches attributes of detected/monitored activities, jobs and tasks as in [0042], status reports being generated monthly as in [0044], and a memory/database being used to store the information generated in the system as in [0152].
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the activities and tasks of activities of Lassoued with the detected and monitored activities of Meyerzon as they are both analogous art along with the claimed invention which teach solutions to attributing work to activities, and the combination would lead to an improved system which would improve user experience for users engaging with the system as taught in [0043] of Meyerzon.
The combination of Lassoued and Meyerzon teaches an activity categorizer and generation of activity categories as above.
Lassoued teaches [0092] machine learning and use of a neural network with back propagation, along with using feedback to train the model as in [0088-0092], and
Meyerzon teaches training of the learning model/component using received information as in [0096] as well as use of recurrent and trained neural networks as in [0072], neither explicitly teaches adjusting and training for a desired output based on the received inputs.
Neumann, a method and system for an interactive system for activity quantification, teaches a neural network which is adjusted based on the connections and weights which are received inputs, to produce desired values as in [0036].
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the activities and tasks of activities using machine learning and neural networks of the combination of Lassoued and Meyerzon with the adjusting of weights and connections using a feedback loop of Neumann as they are all analogous art along with the claimed invention which teach solutions to attributing work to activities, and the combination would lead to an improved system which would improve user scores as well as improving user activity as taught in [0050] of Neumann.
Examiner notes Lassoued teaches a system and processor ([0010] computer/system and processor).
Regarding Claims 2, 11, and 17, Lassoued teaches wherein the operations further comprise:
inputting status reports for activity categories for a time period to a report generator, implementing generative artificial intelligence, to generate a general report across activities including information on the activity categories in which the detected user was engaged for the time period from the status reports ([0073] selected time periods are used to correlate data and generate reports as in Claim 1 above).
Regarding Claim 4, Lassoued teaches wherein the information on the activity comprises content the detected user entered into an application program during the time period ([0073] information entered in or inputted by user or detected/monitored from user during the time period)
Regarding Claims 6 and 13, Lassoued teaches wherein the information on the activity indicates units of work completed during the activity ([0070] and [0073-74] activities are broken down into tasks).
Regarding Claims 8, 15, and 20, Lassoued teaches use of a machine learning model for inputting information of an activity, activity categories, and detecting user activity of a user for user engagement as in Claim 1 above, but does not explicitly state using a user role.
Meyerzon teaches inputting the information on the activity and a role list of user roles to a role categorizer, implementing a classifier machine learning model, to output a role comprising one of the roles in the role list for the detected user, wherein the output role is additionally inputted to the activity categorizer to output the activity category ([0050] and [0060] a role for a project with attributes and key identifiers which is detected/monitored).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the activities and tasks of activities of Lassoued with the roles for activities of Meyerzon as they are both analogous art along with the claimed invention which teach solutions to attributing work to activities, and the combination would lead to an improved system which would improve user experience for users engaging with the system though the speed being increased as taught in [0043] of Meyerzon.
Allowable Subject Matter
Claims 3, 5, 7, 9, 12, 14, and 18-19 are objected to as being dependent upon a rejected base claim, but would be allowable if the independent claims were amended in such a way as to overcome the 35 USC 101 rejection, contains all intervening claims, and other rejections.
Conclusion
The prior art made of record is considered pertinent to applicant's disclosure.
US 20230401499 A1
Boone; Michael et al.
INCREASING DATA DIVERSITY TO ENHANCE ARTIFICIAL INTELLIGENCE DECISIONS
US 20200367807 A1
LASSOUED; Yassine et al.
INTELLIGENT MONITORING OF A HEALTH STATE OF A USER ENGAGED IN OPERATION OF A COMPUTING DEVICE
US 20220067551 A1
Wei; Daowen et al.
NEXT ACTION RECOMMENDATION SYSTEM
US 20200401983 A1
MEYERZON; Dmitriy et al.
EXTRACTING AND SURFACING USER WORK ATTRIBUTES FROM DATA SOURCES
US 20180082240 A1
Meyerzon; Dmitriy et al.
EXTRACTING AND SURFACING USER WORK ATTRIBUTES FROM DATA SOURCES
US 20120143952 A1
von Graf; Fred
SYSTEM AND METHOD FOR EVENT FRAMEWORK
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/JOSEPH M WAESCO/Primary Examiner, Art Unit 3625B 2/24/2026