DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . The following NON-FINAL office action is in response to Applicant communication filed on 12/01/2025 regarding application 17/440,293. Claims 1, 17, 35-36, 40, 42-43, 45-47 have been amended. Claims 48-49 have been added as new claims. Thus, Claims 1, 3-8, 17, 19-24, 26, 30 and 32-49 have been rejected.
Response to Amendments
2. Applicant’s amendment filed on 12/01/2025 necessitated new grounds of rejection in this office action.
Continued Examination under 37 CFR 1.114
3. 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/01/2025 has been entered.
Response to Arguments
4. Applicant’s arguments, see page 12, filed on 12/01/2025, with respect to the Claim Objections for Claims 1 and 17 have been fully considered and are found to be persuasive. Therefore, the Claim Objections for Claims 1 and 17 are withdrawn.
5. Applicant’s arguments, see pages 12-13 filed on 12/01/2025, with respect to the 35 U.S.C. § 112 (b) Claim Rejections for Claims 1, 3-8, 17, 19-24, 26, 30 and 32-47 have been fully considered and are found to be persuasive. Therefore, the 35 U.S.C. § 112 (b) Claim Rejections for Claims 1, 3-8, 17, 19-24, 26, 30 and 32-47 withdrawn.
6. Applicant’s arguments, see pages 18-19 filed on 12/01/2025, with respect to the 35 U.S.C. § 103 Claim Rejections for Claims 1, 3-8, 17, 19-24, 26, 30 and 32-47 have been fully considered and are found to be not persuasive. Applicant’s arguments with respect to Claims 1, 3-8, 17, 19-24, 26, 30 and 32-49 have been considered, but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Priority
7. The Examiner has noted the Applicants claiming Priority from Provisional PRO 62/835,135 filed on 04/17/2019 and 371 of PCT/FI2020/050250 filed on 04/15/2020. Examiner notes that the earliest effective filing date of this examined application is 04/17/2019.
Response to 35 U.S.C. § 101 Arguments
8. Applicant’s 35 U.S.C. § 101 arguments, filed with respect to Claims 1, 3-8, 17, 19-24, 26, 30 and 32-49 have been fully considered, but they are found not persuasive (see Applicant Remarks, Pages 13-18, dated 12/01/2025). Examiner respectfully disagrees.
Argument #1:
(A). Applicant argues that Claims 1, 3-8, 17, 19-24, 26, 30 and 32-49 do not recite an abstract idea, law of nature of natural phenomenon under revised step 2a prong one of the 35 U.S.C. § 101 analysis (see Applicant Remarks, Pages 13-14, dated 12/01/2025). Examiner respectfully disagrees.
Specifically, Applicant argues that the amended claim limitations of Independent Claims 1 and 17 do not recite a mental process when they contain limitations that cannot practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations. Applicant states that the present claims cannot be performed mentally, or with pen and paper. Examiner respectfully disagrees.
In response to Applicant’s remarks, Examiner notes that Independent Claims 1 and 17 describes a process involving automated meeting scheduling using AI and accessing data from mobile devices. The described process centers on: Receiving meeting requests, accessing various data sources (calendars, location, emails, cloud accounts) from users' mobile devices, using artificial intelligence to perform calculations (like weighted averages) to find a meeting time and place that minimizes disruption or increases efficiency, generating an output of the scheduled meeting details, collecting private data and updating AI models based on this data, ensuring private personal schedule data is not revealed to other users, identifying blank spaces in the calendar as free and collecting private data and update the AI models to develop and understanding of the scheduling and meeting preferences of one or more other meeting participants. In the context of U.S. patent law, activities related to organizing human activity, managing data, and performing calculations are often considered abstract ideas. The core concepts here include: Scheduling and Organizing Information: The fundamental practice of finding a suitable time and place for a meeting. Data Collection and Analysis: Gathering data from multiple sources and performing calculations to determine an outcome. Automation of a Known Process: Using computers and AI to automate steps that could, in theory, be performed by humans, such as comparing schedules and suggesting times. The described process is likely directed to the abstract idea of automated meeting scheduling, data management, and analysis. Independent Claims 1 and 17 recite "performing a weighted average calculation": This directly recites a mental process of a numerical evaluation, which is an enumerated abstract idea. The steps of "receiving a request for schedule calculation", "accessing calendars", "determining at least one of a meeting means or place, and a meeting time", "generating an output": These steps describe a process that can be characterized as a mental process that could, in principle, be performed by a human mind (a human could manually check calendars, calculate availability, and suggest times/places). The use of AI or a generic computer for this process does not automatically remove it from the "mental process" grouping at this stage of the analysis. Examiner notes that mental processes of the use of "weighted average calculation" to determine a schedule. Methods of Organizing Human Activity: The core function of scheduling meetings, coordinating personal data (locations, calendars, preferences), and optimizing efficiency for human interaction and behavior. Mental Processes: The steps involved in evaluating schedules and determining an optimal meeting time/place could be seen as steps that can practically be performed in the human mind, even if laboriously, with pen and paper.
Additionally, the steps of "understanding schedules and meeting preferences of the at least two users", "avoiding disruption... or increasing efficiency for the meeting": These steps are related to managing or coordinating interactions and personal behavior between people, which falls under the category of certain methods of organizing human activities. Also "identifying blank spaces in the calendar as free": This is a classic example of a mental process that can be performed by a human mind, such as looking at a calendar and identifying open slots. The step of "collecting the private data and updating the one or more artificial intelligence models": Data collection and updating models are considered generic computer functions and fundamental building blocks of mathematical concepts (algorithms/models). The abstract idea here is the underlying algorithm and the process of gathering information and using it to adjust a predictive tool. Lastly, the step of "develop the understanding of the schedules and the meeting preferences": This is an abstract human activity or mental process of understanding preferences and schedules. It relates to the general concept of planning and optimization, a type of business method or mental process. Mental Processes: The activities of identifying free time and understanding preferences are tasks that humans perform when scheduling meetings. Automating these well-known human mental processes on a computer, without more, often falls into this category. methods of organizing human activities: The use of "one or more artificial intelligence models" falls under the broad category of methods of organizing human activities, which are patent-ineligible in themselves. Merely using a generic AI model for a known purpose (scheduling) does not typically remove it from the abstract idea category at this stage.
Additionally, with respect to the “Certain Methods of Organizing Human Activities Grouping” for amended Independent Claims 1 and 17, Examiner refers Applicant to MPEP § 2106.04 (a)(2) II which states that: “Finally, the sub-groupings encompass both activity of a single person (for example, a person following a set of instructions or a person signing a contract online) and activity that involves multiple people (such as a commercial interaction), and thus, certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the "certain methods of organizing human activity" grouping. It is noted that the number of people involved in the activity is not dispositive as to whether a claim limitation falls within this grouping. Instead, the determination should be based on whether the activity itself falls within one of the sub-groupings.”
With respect to “Mental Processes” category, Examiner refers Applicant to MPEP § 2106.04 (a) (2) (III) (C): “Claims can recite a mental process even if they are claimed as being performed on a computer. “For instance, the Examiner has reviewed Applicant’s Specification and determined that the claimed invention is described as concepts that are performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer (see Applicant’s Specification ¶ [0081]: “The processor 204 may also be implemented as a processor set comprising, for example, a general-purpose microprocessor and a math or graphics co-processor.”), or 2) in a computer environment (see Applicant’s Specification ¶ [0094]: “The cloud server 222 may be deployed in a cloud environment managed by a cloud storage service provider, and the databases may be configured as cloud-based databases implemented in the cloud environment.”), or 3) is merely using a computer as a tool (e.g., see Applicant’s Specification ¶ [0059]: “The cloud based intelligent secretary application is henceforth explained to utilize Artificial Intelligence for several purposes. Known machine learning tools/deep learning frameworks may be utilized with or without modifications.”) to perform these concepts.” Thus, based on these 3 factors, Examiner maintains that the claims still recite a mental process. Also, Examiner refers Applicant to MPEP § 2106.04 (a) III (B): “The use of a physical aid (e.g., pencil and paper or a slide rule) to help perform a mental step does not negate the mental nature of the limitation, but simply accounts for variations in memory capacity from one person to another. For instance, in CyberSource, the court determined that the step of "constructing a map of credit card numbers" was a limitation that was able to be performed "by writing down a list of credit card transactions made from a particular IP address." The use of "physical aids" in implementing the abstract mental process, does not preclude the claim from reciting an abstract idea. See MPEP § 2106.04(a) III C.
Applicant asserts that the Patent Office has not presented objective evidence on how Independent Claims 1 and 17 could be carried out as a mental process (see Applicant Remarks, Page 17, dated 12/01/2025). Examiner respectfully disagrees.
See 35 U.S.C. § 101 analysis shown above for step 2a prong 1 regarding that certain/particular steps of Independent Claims 1 and 17 recite “Mental Processes”. Additionally, Examiner refers Applicant to MPEP § 2106.07 III: “Evidentiary Requirements in Making a 35 U.S.C. § 101 Rejection”-> Thus, the court does not require "evidence" that a claimed concept is a judicial exception, and generally decides the legal conclusion of eligibility without resolving any factual issues. FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1097, 120 USPQ2d 1293, 1298 (Fed. Cir. 2016) (citing Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1373, 118 USPQ2d 1541, 1544 (Fed. Cir. 2016)); OIP Techs., 788 F.3d at 1362, 115 USPQ2d at 1092; Content Extraction & Transmission LLC v. Wells Fargo Bank, N.A., 776 F.3d 1343, 1349, 113 USPQ2d 1354, 1359 (Fed. Cir. 2014). When performing the analysis at Step 2A Prong One, it is sufficient for the examiner to provide a reasoned rationale that identifies the judicial exception recited in the claim and explains why it is considered a judicial exception (e.g., that the claim limitation(s) falls within one of the abstract idea groupings). Therefore, the 35 U.S.C. § 101 analysis step 2a prong 1 analysis concludes that Independent Claims 1 and 17 are directed to one or more abstract ideas because the limitations primarily describe the implementation of the abstract concept of scheduling and data analysis using generic computing components (mobile devices, AI algorithms) without claiming an improvement to the underlying computer technology or a specific technical solution to a technical problem outside the abstract idea itself. In conclusion, Examiner maintains that Claims 1, 3-8, 17, 19-24, 26, 30 and 32-49 still recite an abstract idea under either “Certain Methods of Organizing Human Activities” or “Mental Processes” under 35 U.S.C. § 101 Step 2A Prong 1. Thus, Claims 1, 3-8, 17, 19-24, 26, 30 and 32-49 are ineligible with respect to the 35 U.S.C. § 101 analysis.
Argument #2:
(B). Applicant argues that Claims 1, 3-8, 17, 19-24, 26, 30 and 32-49 do not recite an abstract idea, law of nature of natural phenomenon under revised step 2a prong one of the 35 U.S.C. § 101 analysis and cites Example 39 of the 35 U.S.C. § 101 Examples (see Applicant Remarks, Pages 13-14, dated 12/01/2025). Examiner respectfully disagrees.
Example 39 of the 35 U.S.C. 101 Examples is a method for training a neural network for facial detection. In this claim, the Applicant addressed the issue of detecting human faces where there are shifts, distortions and variations of the face pattern in the image. This Example 39 contained the additional elements such as “applying one or more transformations”, “training the neural network in stage 1” & “training the neural network in stage 2”, in the steps such as “applying one or more transformations to each digital facial image…”, “creating a first training set comprising the collected set of digital facial images…”, “training the neural network in a first stage…”, “creating a second training set for a second stage of training comprising the first training set…” and “training the neural network in a second stage…”, wherein the steps cannot be categorized as reciting judicial exceptions. Specifically, Example 39 used neural networks to perform facial detection. A neural network is a framework of machine learning algorithms that work together to classify inputs based on a previous training process. These are automated steps that are performed by computer using machine learning algorithm software code (emphasis added). Thus, Example 39 was deemed as patent eligible under step 2a prong 1 as not reciting neither a “Mental Process” or “Certain Method of Organizing Human Activities”.
Additionally, the claims of the instant application recite a different fact pattern as shown in Example Claim 39 Subject Matter Eligibility Examples of “Method for Training a Neural Network for Facial Detection”. Here, the instant claims of the current application are trying to solve a business problem in order to identify free time and understanding preferences in calendars which are tasks that humans perform when scheduling meetings. Automating these well-known human mental processes on a computer, without more, often falls into this category. Certain Methods of Org Human Activities: The use of "one or more artificial intelligence models" falls under the broad category of certain methods of org human activities, which are patent-ineligible in themselves. Merely using a generic AI model for a known purpose (scheduling) does not typically remove it from the abstract idea category at this stage. Also "identifying blank spaces in the calendar as free": This is a classic example of a mental process that can be performed by a human mind, such as looking at a calendar and identifying open slots. The step of "collecting the private data and updating the one or more artificial intelligence models": Data collection and updating models are considered generic computer functions. The core idea of identifying empty slots on a calendar (whether paper or digital) is an activity that a human could perform mentally or with pen and paper, i.e., "looking at a calendar and finding open times". A Convolutional Neural Network (CNN) is a mathematical model that uses mathematical calculations (e.g., filter optimization, backpropagation, gradient descent) to process data and make predictions (e.g., identifying empty slots). Claims that specify the use of such algorithms are generally considered to recite a mathematical concept, which is an abstract idea.
The problem being solved in Example 39 Subject Matter Eligibility Examples of “Method for Training a Neural Network for Facial Detection” is an improvement to computer technology for identifying human faces in digital images. The problem being solved in Example 39 Subject Matter Eligibility Examples of “Method for Training a Neural Network for Facial Detection” is an improvement to computer technology for identifying human faces in digital images. This technology has several different potential uses, ranging from tagging pictures in social networking sites to security access control. Some prior methods use neural networks to perform facial detection. Also the claim of Example 39 recited the additional steps of (1) a specific technological transformation -> “applying one or more transformations to each digital facial image including mirroring, rotating, smoothing, or contrast reduction to create a modified set of digital facial images”, (2) “creating a first training set comprising the collected set of digital facial images, the modified set of digital facial images, and a set of digital non-facial images” & “training the neural network in a first stage using the first training set” and (3) retraining the neural network for a 2nd time by the steps of -> “creating a second training set for a second stage of training comprising the first training set and digital non-facial images that are incorrectly detected as facial images after the first stage of training; and training the neural network in a second stage using the second training set.”
Examiner notes that Independent Claims 1 and 17 is readily distinguishable from Example 39 both in terms of its (1) data inputs and (2) “how” the analysis is performed. In Example 39, the data used to train the model are “digital facial images” to which transformations are applied including “mirroring, rotating, smoothing, or contrast reduction to create a modified set of facial images.” Applicant does not contend that it invented artificial intelligence or neural networks in general or any such algorithm in particular. And, in the absence of any specific implementation details, Examiner is not persuaded that steps of “processing one or more images of the calendars from the at least two users… to identify one or more empty calendar slots in the calendars” as recited using a convolutional neural network (CNN) to generate outputs, and therefore an abstract idea.
Argument #3:
(C). Applicant argues that Claims 1, 3-8, 17, 19-24, 26, 30 and 32-49 recite additional elements that integrate the judicial exception into a practical application under revised step 2a prong two of the 35 U.S.C. § 101 analysis (see Applicant Remarks, Pages 14-17, dated 12/01/2025). Examiner respectfully disagrees.
Specifically, Applicant argues that the amended claim limitations for Independent Claims 1 and 17 are patent eligible over step 2a prong 2 due to “providing the best meeting time, place and form” and further “improves privacy, because the cloud server can record the calendars of the participants and calculate and optimum meeting parameters without revealing contents of the other user’s calendars to meeting participants. These technical improvements and practical applications are clearly enabled by the present claims and these claims implement these data security and privacy improvements at least by including that “the private data reflecting a personal schedule of a first user, of the at least two users, is not revealed to a second user, of the at least two users.” Examiner respectfully disagrees.
In response to Applicant’s remarks here for step 2a prong 2, Examiner notes that Independent Claims 1 and 17 mandate the use of "respective mobile devices," "artificial intelligence models," "one or more cloud accounts," "a calendar," and "email." These are all generic, conventional computing components used in a standard manner to perform their expected functions (data access, processing, output). Merely saying "do it on a mobile device" or "use AI" does not add an inventive concept that integrates the abstract idea into a new, practical application. No Technical Improvement: The claims do not describe a technical improvement to the underlying technology itself. For example, there is no claim for a new, non-abstract AI model, a novel mobile device component, or an improved data transmission protocol. The efficiency gained is in the result (scheduling a meeting faster), not in the technological process that achieves it. Mere Automation of Human Activity: The entire process—collecting availability data, considering preferences, performing a calculation, and selecting a time/place—mimics what a human personal assistant or individual would do manually. Automating a known human activity using general-purpose computers does not constitute a "practical application" as defined by patent law (e.g., Alice Corp. Pty. Ltd. v. CLS Bank Int'l). Generic "Information Gathering" and "Output": The steps of "accessing private data," "accessing calendars," and "generating an output" are generic data-gathering and output steps. These are routinely performed in countless computer processes and do not provide the necessary "significantly more" to confer eligibility. Privacy Constraint as a Business Rule: The condition that "private data reflecting a personal schedule of a first user... is not revealed to a second user" is a policy or business rule, not a concrete, technical implementation that transforms the nature of the claim.
Also, Examiner notes that merely instructing a computer (or GPU) to perform an abstract idea using generic computing components is not enough to demonstrate a practical application. The use of a GPU for "identifying blank spaces" and "updating AI models" is described only as a high-level function. GPUs are general-purpose processors for linear algebra acceleration, which is conventional for AI tasks. The claim language does not describe a specific technological improvement to the GPU's functioning itself (e.g., a new architecture or a novel training method that improves GPU performance in a unique way). The claims broadly describe the desired outcome (understanding schedules, identifying free time) rather than a particularized implementation that solves a technical problem in a non-abstract way. The steps of "identifying blank spaces" and "collecting private data" are fundamental, preparatory steps for the underlying abstract idea (scheduling and data analysis) and do not provide the necessary "technological solution to a technological problem". The description "output of the updated one or more artificial intelligence models, the operations further comprising, identifying blank spaces..." is a generic "apply it" claim, which takes an abstract idea (scheduling/AI modeling) and applies it to a specific context (calendars/private data) without any further meaningful technical limitations. The claim essentially gathers, analyzes, and outputs data without a specific, integrated, inventive technical feature that is more than just an application of the idea.
Moreover, for Dependent Claims 48-49: These claims apply a known, generic AI technique (CNN) to a conventional task (analyzing calendars) using generic computing hardware (a GPU). The use of a CNN and GPU, while advanced, are considered general-purpose computing components and techniques within the field of computer vision and image processing. The claim does not describe a specific improvement to the functioning of the computer itself (e.g., an improved training method for the CNN, or a novel GPU architecture) or a specific improvement to another technical field beyond automating a conventional human activity (reading a calendar). Simply performing a task "faster" or "more efficiently" using generic AI is not sufficient for eligibility. The underlying activity—looking at a calendar to find an empty slot—is a mental process that can be performed by a human mind, possibly with pen and paper. Merely automating a mental process on a computer using a generic AI model, without more, does not integrate it into a patent-eligible practical application. These claims lack additional, concrete technical steps that would impose a meaningful limitation on the abstract idea. For instance, these claims do not recite specific post-processing actions in a different technical field (e.g., automatically scheduling a specific piece of equipment based on the empty slot, or optimizing a specific network monitoring system) that have been found eligible in other cases. In sum, because the claim involves applying a generic AI method (CNN) on a general-purpose machine (GPU) to automate a fundamental human/mental task (finding empty calendar slots) without any specific, asserted technological improvement to the computer or a separate technical field, it does not integrate the judicial exception into a practical application and thus is likely patent ineligible under Step 2A, Prong 2 of the USPTO guidance.
Therefore, these claims when factoring the shown when considered individually and as an ordered combination (as a whole) in view of these claim limitations, these additional elements does not provide limitations that are indicative of integration into a practical application under step 2a prong 2 due to: (1) recites mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions by providing the results to the user on a computer (see MPEP § 2106.05 (f)) or (2) the claims as a whole are limited to a particular field of use or technological environment pertaining to monitoring and analyzing scheduling of meetings by accessing calendar data from multiple members of the group and/or meeting participants and calculating meeting means, place and time that allows a minimum total disruption and cost and/or maximum efficiency for the meeting using a computer in the fields of appointment and/or meeting scheduling (see MPEP § 2106.05 (h)).
In conclusion, Examiner maintains that Claims 1, 3-8, 17, 19-24, 26, 30 and 32-49 as currently recited do not contain additional elements that integrate the judicial exception into a practical application under step 2a prong 2 of the 35 U.S.C. 101 analysis.
Argument #4:
(D). Applicant argues that Claims 1, 3-8, 17, 19-24, 26, 30 and 32-49 recite additional elements that integrate the judicial exception into a practical application under revised step 2a prong two of the 35 U.S.C. § 101 analysis and cites the Appeals Review Panel of the PTAB in Ex parte Desjardins court case (see Applicant Remarks, Pages 17-18, dated 12/01/2025). Examiner respectfully disagrees.
In response to Applicant’s arguments here, Examiner notes that the Ex parte Desjardins decision, the Appeals Review Panel (ARP) focused on claims directed to a computer-implemented method for training a machine learning model to address "catastrophic forgetting". Representative claim 1 described a method involving determining parameter importance for a first task, then training on a second task while adjusting parameters to optimize the second task's performance and protect the first task's performance. The September 26, 2025, decision in Ex parte Desjardins was a significant ruling by the U.S. Patent and Trademark Office (USPTO) Appeals Review Panel (ARP) regarding the patent eligibility of artificial intelligence (AI) and machine learning (ML) inventions. This decision is binding on USPTO examiners and boards, though it does not carry precedential weight in federal courts. Specifically, Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) noted in which the specification identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting,” and that the claims reflected the improvement identified in the specification. Indeed, enumerated improvements identified in the Desjardins specification included disclosures of the effective learning of new tasks in succession in connection with specifically protecting knowledge concerning previously accomplished tasks; allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system. Such improvements were tantamount to how the machine learning model itself would function in operation and therefore not included in the identified mathematical calculation.
In contrast to the Ex parte Desjardins Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) court case, Examiner notes these claims provide no technical detail that is specifically recited in Independent Claims 1 and 17 that explain and describe how the training and/or retrained of the machine learning models or AI models occurs from a technical standpoint. Independent Claims 1 and 17 are not analogous to Ex parte Desjardins court case claims.
Furthermore, for Dependent Claims 48-49, Examiner notes that these claims are interpreted as a limited field of use or technological environment for processing images of the calendars from at least two users in order to identify one or more empty calendar slots in the calendars using a neural network in the field of use of calendar scheduling for meetings (see MPEP § 2106.05 (h)). In conclusion, Examiner maintains that Claims 1, 3-8, 17, 19-24, 26, 30 and 32-49 as currently recited do not contain additional elements that integrate the judicial exception into a practical application under step 2a prong 2 of the 35 U.S.C. 101 analysis and are not analogous to the Ex parte Desjardins court case decision.
Claim Rejections - 35 USC § 101
9. 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.
10. Claims 1, 3-8, 17, 19-24, 26, 30 and 32-49 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1, 3-8, 17, 19-24, 26, 30 and 32-49 are each focused to a statutory category namely, a “non-transitory computer readable medium” or “article of manufacture” (Claims 1, 3-8, 26 and 32-48) and a “system” or an “apparatus” (Claims 17, 19-24, 30 and 49).
Step 2A Prong One: Independent Claims 1 and 17 recite limitations that set forth the abstract idea(s), namely (see in bold except where strikethrough):
“ for schedule calculation of a meeting ” (see Independent Claim 1);
“” (see Independent Claim 17);
“” (see Independent Claim 17);
“receiving a request for schedule calculation of a meeting of at least two users” (see Independent Claims 1 and 17);
“generating a response to the request for schedule calculation of the meeting, based on accessing respective of the at least two users and based on using comprising” (see Independent Claims 1 and 17);
“using private data obtained from the respective to understand schedules of the at least two users” (see Independent Claims 1 and 17);
“accessing calendars from the at least two users” (see Independent Claims 1 and 17);
“performing a weighted average calculation and determining at least one of a meeting means or place, and a meeting time, wherein the schedule calculation of the meeting: (i) avoids disruption to one or more previously scheduled meetings or (ii) increases efficiency for the meeting, by accessing data of the at least two users” (see Independent Claims 1 and 17);
“generating an output comprising the calculated meeting means or place, and the meeting time, to the at least two users” (see Independent Claims 1 and 17);
“generating the response to the request for schedule calculation of the meeting is based on accessing each condition of all of the following from the at least two users: (i) one or more accounts, (ii) a calendar, (iii) an email, (iv) location, and the private data reflecting a personal schedule of a first user, of the at least two users, is not revealed to a second user, of the at least two users” (see Independent Claims 1 and 17);
“generating the response to the request for schedule calculation of the meeting is based on collecting the private data and updating one or more models to develop an understanding of the schedules and meeting preferences of the at least two users, the operations further comprising” (see Independent Claims 1 and 17);
“the generated response to the request is an output of the updated one or more models” (see Independent Claims 1 and 17);
“the operations further comprising” (see Independent Claims 1 and 17);
“identifying blank spaces in the calendar as free ” (see Independent Claims 1 and 17);
“collecting the private data and updating the one or more models to develop the understanding of the schedules and the meeting preferences of one or more other meeting participants that one or more of the at least two users has interacted with, is interacting with, or is planning to interact with” (see Independent Claims 1 and 17).
The steps as shown above recite abstract ideas for scheduling a meeting using artificial intelligence, private user data, and mobile device access. These claims as described fundamentally cover the process of organizing human activity (scheduling a meeting) and performing mental process calculations/data manipulation (weighted averages, determining meeting times/places based on schedules). While using data from mobile devices and AI might seem modern, the underlying concept is simply automating a process that humans (e.g., administrative assistants) have done for centuries: gathering availability, comparing schedules, calculating the best time, and communicating the result.
Therefore, these abstract idea limitations (as identified above in bold), under their broadest reasonable interpretation of the claims as a whole, cover performance of their limitations as “Mental Processes” which pertains to (1) concepts performed in the human mind (including observations or evaluations or judgments or opinions) and/or (2) using pen and paper as a physical aid, in order to help perform these mental steps does not negate the mental nature of these limitations. The use of "physical aids" in implementing the abstract mental process, does not preclude the claim from reciting an abstract idea. See MPEP § 2106.04(a) III C.
Additionally, and/or alternatively, these abstract idea limitations (as identified above in bold), under their broadest reasonable interpretation of the claims as a whole, cover performance of their limitations as “Certain Methods of Organizing Human Activities” which pertains to (3) managing personal behavior or relationships or interactions between people (including teachings or following rules or instructions).
That is, other than reciting (e.g., “one or more processors”, “one or more memories”, “a software program product”, “GPU”, “one or more mobile devices”, “cloud servers”, “cloud” & “application”), nothing in the claim elements precludes the steps from being performed as “Mental Processes” which pertains to (1) concepts performed in the human mind (including observations or evaluations or judgments or opinions) or (2) using pen and paper as a physical aid, and additionally or alternatively as “Certain Methods of Organizing Human Activities” which pertains to (3) managing personal behavior or relationships or interactions between people (including social activities, teachings or following rules or instructions).
Therefore, at step 2a prong 1, Yes, Claims 1, 3-8, 17, 19-24, 26, 30 and 32-49 recite an abstract idea. We proceed onto analyzing the claims at step 2a prong 2.
Step 2A Prong Two: With respect to Step 2A Prong Two of the eligibility inquiry (as explained in MPEP § 2106.04(d)), the judicial exception is not integrated into a practical application. Independent Claims 1 and 17 recites additional elements directed to: (e.g., “one or more processors”, “one or more memories”, “a software program product”, “one or more mobile devices”, “cloud servers”, “GPU”, “cloud” & “application”). These additional elements have been considered individually and in combination, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment. See MPEP § 2106.05(f) and MPEP § 2106.05(h).
Independent Claims 1 and 17: With respect to reliance on (e.g., “artificial intelligence” or “one or more artificial intelligence models”) as an additional element shown in Independent Claims 1 and 17, when considered both individually and as an ordered combination (as a whole), this additional element does not integrate the abstract idea into a practical application under step 2a prong 2 due to: the claims as a whole are limited to a particular field of use or technological environment pertaining to monitoring and analyzing scheduling of meetings by accessing calendar data from multiple members of the group and/or meeting participants and calculating meeting means, place and time that allows a minimum total disruption and cost and/or maximum efficiency for the meeting using a computer in the fields of appointment and/or meeting scheduling (see MPEP § 2106.05 (h)).
Furthermore, in Independent Claims 1 and 17, even if the step of “receiving data over a network” such as (e.g., “receiving a request for schedule calculation of the meeting with at least two users”) is evaluated as an additional element, this activity at most amounts to “mere data gathering” which reflects insignificant extra-solution activities (see MPEP 2106.05 (g)).
In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception. Therefore, at step 2a prong 2, Claims 1, 3-8, 17, 19-24, 26, 30 and 32-49 are directed to the abstract idea and do not recite additional elements that integrate into a practical application.
Step 2B: (As explained in MPEP § 2106.05), it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Independent Claims 1 and 17 recites additional elements directed to: (e.g., “one or more processors”, “one or more memories”, “GPU”, “a software program product”, “one or more mobile devices”, “cloud servers”, “cloud” & “application”). These elements have been considered individually and in combination, but fail to add significantly more to the claims because they amount to using computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (computing environment) and does not amount to significantly more than the abstract idea itself. See MPEP § 2106.05 (f) and MPEP § 2106.05 (h). Notably, Applicant’s Specification suggests that the claimed invention relies on nothing more than a general-purpose computer executing the instructions to implement the invention (see at least Applicant’s Specification ¶ [0081] noting that “The processor 204 may also be implemented as a processor set comprising, for example, a general-purpose microprocessor and a math or graphics co-processor.)
Furthermore, in Independent Claims 1 and 17, even if the step of “receiving data over a network” such as (e.g., “receiving a request for schedule calculation of the meeting with at least two users”) is evaluated as an additional element, this activity at most amount to insignificant extra-solution activities, which has been expressly recognized as well-understood, routing, and conventional (WURC), and thus insufficient to add significantly more to the abstract idea. See MPEP § 2106.05(d) ii - Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359,1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
The additional elements of “artificial intelligence” in Independent Claims 1 and 17 do not amount to significantly more than the judicial exceptions under step 2B due being expressly recognized as Well-Understood, Routine and Conventional (WURC) in the art.
For example; see US PG Pub (US 2016/0098687 A1) – “Systems and Methods for Private Schedule Coordination and Event Planning”, hereinafter Lamons. Lamons at ¶ [0128]: “The decision engine (113) functions as an intelligent software assistant, sometimes called an intelligent software agent, cognitive assistant or cognitive agent in the art. The engine learns, organizes, and correlates data, combining traditionally isolated data mining approaches with artificial intelligence to create a personal-assistant program that improves its predictive accuracy by interacting with its user. Similarly, from a user experience, the client-side software used by the user to interact with the decision engine (113) is generally engineered to behave like an intelligence software assistant, including features such as a voice and plain language interface.
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself.
Dependent Claims 3-8, 19-24, and 26, 30 and 32-49 recite additional elements directed to: (e.g., “machine generated” (Dependent Claims 7 & 23), “productivity tools application” (Dependent Claims 26 & 30), “knowledge base” (Dependent Claims 33-34 and 37) and “artificial intelligence” (Dependent Claims 34, 37, 40-41 and 44) & “weighting and ranking algorithms” (Dependent Claim 44) & “convolutional neural network (CNN)” (Dependent Claims 48-49) & “graphical processing unit (GPU)” (Dependent Claims 48-49)), which in conjunction with the limitations recite the same abstract idea(s) as shown in Independent Claims 1 and 17 along with further steps/details that reflect “Certain Methods of Organizing Human Activities” Grouping which pertains to managing personal behavior or relationships or interactions between people (including social activities, teachings or following rules or instructions) and additionally or alternatively as “Mental Processes” Grouping which pertains to concepts performed in the human mind (including observations or evaluations or judgments) or using pen and paper as a physical aid.
Dependent Claims 2-6, 8, 19-22, 24, 32, 35-36, 38-39, 42-43 and 45-47 further narrow the abstract ideas, and are therefore still ineligible for the reasons previously provided in Steps 2A Prong 2 and 2B for Independent Claims 1 and 17. Dependent Claims 7, 23, 26, 30, 33-34, 37, 40-41, 44 and 48-49: With respect to reliance on (e.g., “machine generated” (Dependent Claims 7 & 23), “productivity tools application” (Dependent Claims 26 & 30), “knowledge base” (Dependent Claims 33-34 and 37) and “artificial intelligence” (Dependent Claims 34, 37, 40-41 and 44) & “weighting and ranking algorithms” (Dependent Claim 44) & “a convolutional neural network” (CNN)” (Dependent Claims 48-49) & “graphical processing unit” (GPU) (Dependent Claims 48-49)) as additional elements shown when considered individually and as an ordered combination (as a whole) in view of these claim limitations, these additional elements does not provide limitations that are indicative of integration into a practical application under step 2a prong 2 and also do not recite additional elements that amount to significantly more than the recited judicial exceptions under step 2B due to: (1) recites mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions by providing the results to the user on a computer (see MPEP § 2106.05 (f)) or (2) the claims as a whole are limited to a particular field of use or technological environment pertaining to monitoring and analyzing scheduling of meetings by accessing calendar data from multiple members of the group and/or meeting participants and calculating meeting means, place and time that allows a minimum total disruption and cost and/or maximum efficiency for the meeting using a computer in the fields of appointment and/or meeting scheduling (see MPEP § 2106.05 (h)).
The additional elements of “convolutional neural network (CNN)” in Dependent Claims 48-49 do not amount to significantly more than the judicial exceptions under step 2B due being expressly recognized as Well-Understood, Routine and Conventional (WURC) in the art.
See NPL Document: "Learning user preferences and understanding calendar contexts for event scheduling”, hereinafter Kim, Donghyeon, et al. Kim at ¶ [abstract]: “we propose Neural Event Scheduling Assistant (NESA) which learns user preferences and understands calendar contexts. The system leverages calendar events for NESA to learn scheduling personal events, and we further utilize NESA for multi-attendee event scheduling. NESA successfully incorporates deep neural networks such as Convolutional Neural Network for learning the preferences of each user and understanding calendar context based on natural languages.”). See also Section 2.3 of Kim “Representation Learning Using Deep Neural Networks.” See also 2.1 “Preference Learning for Event Scheduling.” -> “As Neural Event Scheduling Assistant (NESA) is trained on the Internet standard format, it is generally applicable to other calendar systems.”
The ordered combination of elements in the Dependent Claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself. Therefore, under Step 2B, Claims 1, 3-8, 17, 19-24, 26, 30 and 32-49 do not include additional elements that are sufficient to amount to significantly more than the recited judicial exceptions. Thus, Claims 1, 3-8, 17, 19-24, 26, 30 and 32-49 are ineligible with respect to the 35 U.S.C. § 101 analysis.
Claim Rejections - 35 USC § 103
11. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
12. 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