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 . Claims 1-20 are pending.
Claim Objections
Claim 11 is objected to because of the following informalities: The introductory clause is grammatically incomplete. In particular, “wherein transmitting the message to the computing device to initiate the communication session with the user associated with the plurality of interactions” does not specify the relationship between “transmitting” and the subsequently recited “selecting… configuring...” operations. It appears the intended language is: “wherein… with the plurality of interactions comprises:”. Appropriate correction is required.
Claim Rejections - 35 USC § 112(d)
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 13-19 rejected under 35 U.S.C. 112(d), as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. They recite: “The computer-implemented method of claim(s) [12-13, and/or 17]”, thereby failing to refer to a previous claim and rendering the dependency improper. Claim 12 is a “non-transitory computer-readable medium” rather than a “computer-implemented method”. Applicant may cancel the claim, amend the claim to place the claim in proper dependent form, rewrite the claim in independent form, or present a sufficient showing that the dependent claim complies with the statutory requirements.
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 therefore, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 USC § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 (The Statutory Categories): Is the claim to a process, machine, manufacture or composition of matter? MPEP 2106.03.
Per Step 1, claim 1 is directed to a method (i.e., a process), claim 12 is directed to a non-transitory computer-readable medium (i.e., machine or manufacture), and claim 20 is directed to a system (i.e., machine). Thus, the claims are directed to statutory categories of invention. However, the claims are rejected under 35 U.S.C. § 101 because they are directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The analysis proceeds to Step 2A Prong One.
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon? MPEP 2106.04.
The abstract idea of claims 1, 12, and 20 (claim 1 being representative) is:
receiving a plurality of interactions with an electronic service;
detecting a lack of subsequent interaction with the electronic service that continues longer than a threshold period;
after the detecting, inputting contextual data of the plurality of interactions into an intervention model, the intervention model including weights based on contextual data of past user interaction data and user requests for assistance;
after the inputting, retrieving an output value from the intervention model[;]
determining that the output value is above a threshold value;
based on the determining, transmitting a message to initiate a communication session with a user associated with the plurality of interactions;
receiving an indication that the message was accepted by the user; and
establishing the communication session in response to receiving the indication.
The abstract idea steps italicized above recite determining when to offer assistance to a user and initiating a communication session in response to the user’s acceptance, which constitutes a process that, under its broadest reasonable interpretation (BRI), covers managing personal behavior relationships, interactions between people. This is further supported by paragraph [0068] of applicant’s specification as filed. If a claim limitation, under its BRI, covers managing personal behavior relationships, interactions between people, including social activities, teaching, and/or following rules or instructions, following rules or instructions, then it falls within the Certain Methods of Organizing Human Activity – Managing Personal Behavior Relationships, Interactions Between People grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additionally and alternatively, the claim recites observing and evaluating user interaction information to determine whether assistance should be provided, which could be performed mentally, including with pen and paper. This is further supported by paragraph [0028] of applicant’s specification as filed. If a claim limitation, under its BRI, covers performance of the limitation in the mind, including observations, evaluations, judgements, and/or opinions, then it falls within the Mental Processes – Concepts Performed in the Human Mind grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application? MPEP §2106.04.
This judicial exception is not integrated into a practical application because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP §2106.05(f).
Claim 1 recites the following additional elements: computer-implemented; processing unit; computing device; machine learning.
Claim 12 recites the following additional elements: A non-transitory computer-readable medium comprising instructions, which when executed by a processing unit, configure the processing unit to perform operations comprising; computing device; machine learning.
Claim 20 recites the following additional elements: A system comprising: a processing unit; a storage device comprising instructions, which when executed by the processing unit, configure the processing unit to perform operations comprising; computing device; machine learning.
These elements are merely instructions to apply the abstract idea to a computer, per MPEP §2106.05(f). Applicant has only described generic computing elements in their specification, as seen in paragraphs [0070] – [0074] of applicant’s specification as filed, for example. Further, the combination of these elements is nothing more than a generic computing system.
Accordingly, these additional elements, alone and in combination, do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
Step 2B (The Inventive Concept): Does the claim recite additional elements that amount to significantly more than the judicial exception? MPEP §2106.05.
Step 2B involves evaluating the additional elements to determine whether they amount to significantly more than the judicial exception itself.
The examination process involves carrying over identification of the additional element(s) in the claim from Step 2A Prong Two and carrying over conclusions from Step 2A Prong Two on the considerations discussed in MPEP §2106.05(f).
The additional elements and their analysis are therefore carried over: applicant has merely recited elements that facilitate the tasks of the abstract idea, as described in MPEP §2106.05(f).
Further, the combination of these elements is nothing more than a generic computing system. When the claim elements above are considered, alone and in combination, they do not amount to significantly more.
Therefore, per Step 2B, the additional elements, alone and in combination, are not significantly more. The claims are not patent eligible.
Further, the analysis takes into consideration all dependent claims as well:
Regarding claims 2-11 and 13-19, applicant further narrows the abstract idea with additional step(s). There are no further additional elements to consider, beyond those highlighted above. This further narrowing of the abstract idea, similar to above, is also not patent eligible.
Accordingly, claims 1-20 are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
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, 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 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, 4, 8-9, 11-12, 15, and 19-20 are rejected under 35 U.S.C. § 103 as being unpatentable over Gardner (US 10951761 B1) in view of Rey (US 20220166731).
Claims 1, 12, and 20
Gardner discloses:
(claim 1) A computer-implemented method comprising: {“Disclosed herein is a computer-implemented method for operating a virtual computer assistant (VCA) system of a service provider system.” [0004]}
(claim 12) A non-transitory computer-readable medium comprising instructions, which when executed by a processing unit, configure the processing unit to perform operations comprising: “Disclosed herein is also a non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a processor, cause the processor to execute the method described above.” [0006]}
(claim 20) A system comprising: a processing unit; a storage device comprising instructions, which when executed by the processing unit, configure the processing unit to perform operations comprising: {“Disclosed herein is also a virtual computer assistant (VCA) system having a VCA that is an automated support system to interact with a user… The system further has a switch rules database stored in the memory. The database comprises switch instructions to operate the switch. The controller is connected to the switch rules database, and utilizes the switch instructions to operate the switch in order to connect the user link connection to the VCA connection when the instructions indicate a connection to the VCA connection, and connect the user link connection to the HA connection when the instructions indicate a connection to the HA connection.” [0005]}
receiving, using a processing unit, a plurality of interactions with an electronic service from a computing device; {The system supports a customer communicating with the service provider system through a user communications interface and further supports the monitoring current and historical interactions with the customer. [0013], [0019], [0024]}
determining that the output value is above a threshold value; {The system supports implementing rules when an emotion meets a threshold and switching when a calculated confidence level crosses a predetermined threshold. [0026], [0033]}
Gardner does not disclose, however, Rey, in a similar field of endeavor directed to intelligently responding to queries directed to a user when the user is not available to answer the queries, teaches:
detecting, with the processing unit, a lack of subsequent interaction with the electronic service that continues longer than a threshold period; “the contextual document data may be transmitted, when the user has been inactive for a specified time period or the user utilizes a status notification mechanism to indicate their unavailability.” [0040]; “If a response to the message is not provided within a given time period (e.g., 10 minutes) or it is otherwise determined that the recipient is unavailable to respond (e.g., via a status notification such as an out of office notice or a specific period of inactivity), the communication service 112 may begin examining the message to determine if it is a query to which a response can be provided in the absent of the recipient.” [0044]}
after the detecting, inputting contextual data of the plurality of interactions into an intervention machine learning model, the intervention machine learning model including weights based on contextual data of past user interaction data and user requests for assistance; {“The contextual document data and user-specific data 154 may be transmitted to an indexing model 150 for mapping a degree of association between the received documents and/or information, the user, other documents and/or other individuals. The indexing model 150 may be a trained ML model that is trained to create an association map for the content (e.g., documents) the user has been interacting with (e.g., within the last active session and/or since the last time information for the user was transmitted)” [0041]; The system supports identifying “patterns in user activity and/or determining associations between various actions and commands”, training from data accumulated from “a large number of users over time”, and training approaches including “weighting of training data items.” [0028] – [0029]}
after the inputting, retrieving an output value from the intervention machine learning model; {The indexing model receives contextual data as input and provides “a degree of association map and/or other labels” as output. The access determination service similarly provides an access determination as an output. [0042], [0047]}
based on the determining, transmitting a message to the computing device to initiate a communication session with a user associated with the plurality of interactions; {When the recipient is determined unavailable, an indication is displayed notifying the sender and asking whether the sender would like “a response from the conversation agent”. [0057]}
receiving an indication that the message was accepted by the user; and {“The sender may then be able to utilize the UI elements 215 to indicate their desire to receive a response from the conversation agent or utilize the UI element 220 to choose not to receive a response from the conversation agent.” [0057]}
establishing the communication session in response to receiving the indication. {“Once the sender indicates that they are interested in receiving a response from the conversation agent or in implementations where no notification is presented, the response may be displayed to the sender.” [0058]}
Therefore, it would have been obvious to one of the ordinary skills in the art to modify the user-interaction monitoring and intervention features of Gardner to include the ML user interaction and communication features of Rey, to improve communication and collaboration efficiency. (See paragraph [0021] of Rey).
Claims 4 and 15
The combination of Gardner, Rey, and Pramod the limitations set forth above. Gardner further discloses:
wherein a first computing device of the number of computing devices is of a first type and a second computing device of the number of computing devices is a second type, wherein the first type and second type are different types of computing devices. {An end user may have “various computing devices” including smartphones, tablets, laptops, desktops, and others. [0055] – [0056]}
Claims 8 and 19
The combination of Gardner, Rey, and Pramod the limitations set forth above. Rey further teaches:
wherein the contextual data includes physiological behavioral characteristics of the plurality of interactions. {The system includes biometric components that detect “facial expressions, vocal expressions, hand or body gestures, or eye tracking”, and measure “heart rate or brain waves”. [0090]}
The motivation and rationale to include the additional features of Rey is the same as set forth previously.
Claim 9
The combination of Gardner, Rey, and Pramod the limitations set forth above. Gardner further discloses:
wherein the contextual data includes telemetry data of the plurality of interactions. {The system determines context using location, GPS, cellphone use detection, environmental microphones, and machine sensors including compass and acetometers. [0020], [0048]}
Claim 11
The combination of Gardner, Rey, and Pramod the limitations set forth above. Gardner further discloses:
selecting, using the processing unit, a communication channel of a plurality of communication channels; and {The controller selects the form or mode of communication from multiple available communication channels, including “voice, text, messaging, SMS”, and may switch between voice and digital or text communications. [0042]}
configuring the communication channel to establish the communication session. {The controller operates the switch to connect the customer through the selected communication mode, establishing a connection between the customer and the human assistant or VCA. [0016], [0045] – [0046]}
Claims 2, 5-7, 13, and 16-18 are rejected under 35 U.S.C. § 103 as being unpatentable over the combination of Gardner and Rey, in further view of Pramod (US 20230104757).
Claims 2 and 13
While the combination of Gardner and Rey teaches the limitations set forth above, it does not explicitly teach, however, Pramod, in a similar field of endeavor directed to input classification and response using generative neural networks, teaches:
wherein the contextual data of the plurality of interactions includes a number of computing devices of the user communicating with the electronic service during a period of time. {The system supports contextual activity data identifying a user’s actively communicating networked devices over one or more time intervals and predefined timestamp intervals. [0090] – [0091], [0148]}
Therefore, it would have been obvious to one of the ordinary skills in the art to modify the combination of Gardner and Rey to include the machine learning model weight updating features of Pramod, to improve the performance and adaptability of the ML model. (See paragraph [0062] of Pramod).
Claims 5 and 16
While the combination of Gardner and Rey teaches the limitations set forth above, it does not explicitly teach, however, Pramod, in a similar field of endeavor directed to input classification and response using generative neural networks, teaches:
wherein inputting contextual data of the plurality of interactions into the machine learning model comprises encoding the contextual data into a vector format. {The system converts interaction information into a unified sequence of vectors for processing by a ML model. [0032], [0035]}
The motivation and rationale to include the additional features of Pramod is the same as set forth previously.
Claims 6 and 17
While the combination of Gardner and Rey teaches the limitations set forth above, it does not explicitly teach, however, Pramod, in a similar field of endeavor directed to input classification and response using generative neural networks, teaches:
classifying the plurality of interactions as a task type; and {The system classifies interaction data according to interaction type and clusters interactions representing different activity patterns. [0070], [0101]}
inputting the task type into the intervention machine learning model with the contextual data. {The system combines an indication of the interaction type with contextual position/time information in the same vector representation and provides that combined representation to downstream ML models. [0035]}
The motivation and rationale to include the additional features of Pramod is the same as set forth previously.
Claims 7 and 18
The combination of Gardner, Rey, and Pramod the limitations set forth above. Rey further teaches:
wherein the contextual data includes a level of progress within the task type. {“The information transmitted from the applications 128 may be referred to as contextual document data and may include information about the user's interactions with the document… the lifecycle stage of the document (e.g., authoring, editing, reviewing, or viewing stage).” [0037]}
The motivation and rationale to include the additional features of Rey is the same as set forth previously.
Claims 3 and 14 are rejected under 35 U.S.C. § 103 as being unpatentable over the combination of Gardner, Rey, and Pramod, in further view of Somech (US 20240311563).
Claims 3 and 14
While combination of Gardner, Rey, and Pramod the limitations set forth above, it does not explicitly teach, however, Somech, in a similar field of endeavor directed to enriching language model input with contextual data, teaches:
wherein the contextual data includes a sequence of the number of computing devices communicating with the electronic service during the period of time. {The system supports contextual data comprising user activity information from multiple identified computing devices, where device data is collected continuously and monitored over time. [0034], [0036], [0039] – [0041]}
Therefore, it would have been obvious to one of the ordinary skills in the art to modify the combination of Gardner, Rey, and Pramod to include the adaptive machine learning model training features of Somech, to improve the accuracy of ML predictions. (See paragraph [0095] of Somech).
Claim 10 is rejected under 35 U.S.C. § 103 as being unpatentable over the combination of Gardner and Rey, in further view of Somech (US 20240311563).
Claim 10
While the combination of Gardner and Rey teaches the limitations set forth above, it does not explicitly teach, however, Somech, in a similar field of endeavor directed to enriching language model input with contextual data, teaches:
updating the weights of the intervention machine learning model based on receiving the indication that the message was accepted by the user. {Model weights are adjusted through training using new data to change weights/neural node connections for future predictions. The system supports transfer learning using new feedback. [0024], [0100] – [0102], [0105]}
The motivation and rationale to include the additional features of Somech is the same as set forth previously.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure (additional pertinent references can be found on attached form PTO-892):
US 20190042976 A1, which teaches: Systems, methods, and non-transitory computer readable media can determine one or more actions that a user is likely to take on a page associated with a social networking system, based on one or more first machine learning models. One or more card types that correspond to the one or more actions can be ranked based on a second machine learning model. One or more cards can be generated based on the ranked card types, and each card can include a recommended action associated with the page.
US 20230214244 A1, which teaches: Embodiments of the present disclosure provide for identification and output of improved predicted operational support data object(s). Embodiments utilize particular data sets and data model implementations to identify and select predicted operational support data object(s) determined as associated with the highest confidence to assist in resolving a particular malfunction affecting a networked device in a dynamic home communications network. Such embodiments enable resolution of the malfunction, utilizing the predicted operational support data object(s), with improved success rates and without requiring performance of additional and/or alternative support processes.
US 20210201244 A1, which teaches: Systems and methods are provided for dynamic generation of staff analytics and forecasts based on skill and service level. Dynamic forecasting allows for forecast generation in real-time and may be based on historical data regarding skills and results, as well as data science to identify patterns and make predictions. The resulting staffing forecast may therefore provide for efficient management of messaging staff costs while preserving the desired service quality. The staffing forecast may include a volume forecast that is tailored to the unique nature of asynchronous messaging, as well as the unique messaging needs of the entity so as to efficiently manage messaging operations and make data-driven staffing decisions that take service level into account. An exemplary embodiment may include dynamic analytics tools that may use specified target and/or resource numbers (e.g., desired service level) for an existing messaging operation and get a detailed per-skill staffing forecast.
“Artificial Intelligence ―Creating Automated Insights for Customer Relationship Management (NPL attached), which teaches: Artificial Intelligence (AI) is finally ready to challenge the human intelligence. Computers defeated human in complicated board games like Chess and Chinese game Go. AI is now ready to write poems and complex codes, predict choices, interact with human in real time, mining trillions of data and providing solutions in less than milliseconds. Today AI is driving almost every business unit and Customer Relationship Management (CRM) is one area that is benefitting the most in leveraging better customer experience (CX). In this research paper, researchers studied various AI enabled tools and finalized five AI enabled tools to study the Consumer Awareness, Effectiveness and Loyalty. They used survey based research methodology and collected primary data through Google form. After the data analysis researchers found that consumers are aware of the AI enabled tools and the tools under study are all effective and consumers are also loyal towards the tools.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CARLOS F MONTALVO whose telephone number is (703)756-5863. The examiner can normally be reached Monday - Friday 8:00AM - 5:30PM; First Fridays OOO.
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/C.F.M./Examiner, Art Unit 3629 /SARAH M MONFELDT/Supervisory Patent Examiner, Art Unit 3629
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