DETAILED ACTION
Status of the Application
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 is a Non-Final Office Action in response to claims filed on December 20, 2022.
Claims 1-20 are currently pending and have been examined.
Information Disclosure Statement
The Information Disclosure Statement filed on January 4, 2023 has been considered. Initialed copies of the Form 1449 are enclosed herewith.
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 an abstract idea without significantly more.
Step 1: Is the claim to a process, machine, manufacture or composition of matter? (MPEP 2106.03)
In the present application, claims 1-10 are directed to a method (i.e., a process), claims 11-19 are directed to a computer product (i.e. an article of manufacture), and claim 20 is directed to an apparatus (i.e., a machine). Thus, the eligibility 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)
While claims 1, 11, and 20, are directed to different categories, the language and scope are substantially the same and have been addressed together below.
The abstract idea recited in claims 1, 11, and 20, is directed to classifying communication, comprising the steps:
training at least one machine learning computer model, based on electronic communication features extracted from a plurality of historical electronic communications and actions taken by a user in response to each historical electronic communication in the plurality of historical electronic communications, as specified in a training dataset, wherein the at least one machine learning computer model is trained to predict an action classification that specifies a predicted action that the user will take in response to receiving electronic communications;
receiving a new communication;
extracting communication features of the new communication;
processing, the communication features of the new communication to generate a predicted action classification for the new communication; and
generating an action recommendation output specifying a recommended action to take corresponding to the predicted action classification for the new communication.
Under the broadest reasonable interpretation, without the recitation of additional elements, the limitations above recite steps similar to collecting historical data regarding user behavior (i.e., training), analyzing a new information (i.e., extracting feature), classifying the information based on the historical data (i.e., predicting an action), and suggesting or performing an action based on that classification (i.e., recommendation). These steps are similar to collecting information and analyzing the information. Because the limitations above closely follow the steps of collecting information and analyzing the collected information, and the steps involved human judgements, observations, and evaluations that can be practically or reasonably performed in the human mind, the claims recite an abstract idea consistent with the “mental processes” grouping of the abstract ideas, set forth in MPEP 2106.04(a)(2)(III).
Additionally and alternatively, the same claim limitations above recite a fundamental practice of organizing human activity of managing personal behavior, specifically related to management of interpersonal communications and task prioritization. The process of reviewing a communication, comparing it to past experiences, and deciding on a course of action (e.g., I usually delete emails from this sender or I should forward this to my supervisor) is a task that has long been performed by human assistants and secretaries. Thus, the claims recite an abstract idea consistent with the “certain methods of organizing human activity” grouping of the abstract ideas, set forth in MPEP 2106.04(a)(2)(II).
Accordingly, the above-mentioned limitations are considered as a single abstract idea, therefore, the claims recite an abstract idea and the analysis proceeds to Step 2A. prong two.
Step 2A. prong two: 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 merely add instructions to apply the abstract idea to a computer.
The additional elements considered include:
Claim 1: “a computing device”, “at least by executing a machine learning training operation on the at least one machine learning computer model”, “electronic communication via one or more data networks” and “by the trained at least one machine learning computer model,”
Claim 11: “A computer program product comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a computing device, causes the computing device to:”; “at least by executing a machine learning training operation on the at least one machine learning computer model”, “electronic communication via one or more data networks” and “by the trained at least one machine learning computer model,”
Claim 20: “An apparatus comprising: at least one processor; and at least one memory coupled to the at least one processor, wherein the at least one memory comprises instructions which, when executed by the at least one processor, cause the at least one processor to:”; “at least by executing a machine learning training operation on the at least one machine learning computer model”, “electronic communication via one or more data networks” and “by the trained at least one machine learning computer model,”
In particular, the claim only recites the above-mentioned additional elements to receiving, training, extracting, predicting, and generating information. The computer in the steps is recited at a high-level of generality (i.e., as generic computer components performing a generic computer function; See Applicant’s Specification at least at paragraphs [0032], [0071]-[0076]) such that it amounts to no more than mere instructions to apply the exception using a generic computer component.
That is, the limitations in [A]-[E] are steps of adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea as discussed in MPEP 2106.05(f). The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer. Accordingly, even in combination, these additional element(s) do not integrate the abstract idea into a practical application because they do not improve a computer or other technology, do not transform a particular article, do not recite more than a general link to a computer, and do not invoke the computer in any meaningful way; the general computer is effectively part of the preamble instruction to “apply” the exception by the computer. Therefore, the claims are directed to an abstract idea and the analysis proceeds to Step 2B.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? (MPEP 2106.05)
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the bold portions of the limitations recited above, were all considered to be an abstract idea in Step2A-Prong Two. The additional elements and analysis of Step2A-Prong two is carried over. For the same reason, these elements are not sufficient to provide an inventive concept. Applicant has merely recited elements that instruct the user to apply the abstract idea to a computer or other machinery. When considered individually and in combination the conclusion, as discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer to perform the above-mentioned limitations [A]-[E] amount to no more than mere instructions to apply the function of the limitations to the exception using generic computer component, as discussed in MPEP 2106.05(f). The claim as a whole merely describes how to generally “apply” the concept for classifying communication. Thus, viewed as a whole, nothing in the claim adds significantly more (i.e. an inventive concept) to the abstract idea. For these reasons there is no inventive concept in the claims and thus are ineligible.
As for dependent claims 2-10 and 12-19, these claims recite limitations that further define the abstract idea noted in the independent claims 1 and 11. Claims 2 and 12 further recite addition abstract information regarding the electronic communication features; Claims 3-6, 8, 9, 13-16, 18, and 19 further recite additional abstract steps of determining, forwarding, prioritizing outputting, grouping, receiving, updating, and retraining information; Claims 7 and 17 further recite addition abstract information regarding the previously received electronic communications; Claim 10 further recites abstract information regarding the new electronic communication. These additional abstract information and steps do not change the abstract idea of the independent claim. The claims recite the additional element of computer components at a high level of generality (i.e. as a generic computer system performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using a generic computer component, as discussed in MPEP 2106.05(f). Even in combination, these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. These claims are ineligible.
In summary, the dependent claims considered both individually and as ordered combination do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. The claims do not recite an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or provide meaningful limitations beyond generally linking an abstract idea to a particular technological environment. Therefore, claims 1-20 are rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 102
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 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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-8, 10-18, and 20 are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Winn et al. (US 20130159408 A1).
Claims 1, 11, and 20, Winn discloses (Claim 20) apparatus comprising: at least one processor; and at least one memory coupled to the at least one processor, wherein the at least one memory comprises instructions which, when executed by the at least one processor (para. [0024], [0025], and [0069]);
(Claim 11) a computer program product comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a computing device, causes the computing device to (Claim 17 and para. [0025]):
(Claim 1) a computer-implemented method (para. [0025], computer-implemented process (method)), in a computing device, that provides an action recommendation for a received electronic communication (Abstract; para. [0021] “system may be configured to monitor and observe a user's interactions with incoming data”), comprising:
training at least one machine learning computer model, at least by executing a machine learning training operation on the at least one machine learning computer model based on electronic communication features extracted from a plurality of historical electronic communications and actions taken by a user in response to each historical electronic communication in the plurality of historical electronic communications, as specified in a training dataset (Para. [0021], “training model may be used to synthesize the learnings from observing past data and the behaviors taken on it.” Abstract and Para. [0035]-[0043], disclosing trainer component or trainer 214 for training the prediction system to analyze the data extracted to identify patterns of actions the user may take based on user’s interactions with incoming data), wherein the at least one machine learning computer model is trained to predict an action classification that specifies a predicted action that the user will take in response to receiving electronic communications (Abstract, para. [0005], [0031], [0033], [0038] disclosing the classifier determines the probability a user may take a particular action and generates a predicted action response);
receiving a new electronic communication via one or more data networks (para. [0027]-[0029] disclosing cloud-based network for receiving incoming data and delivering data to one or more users. Communication data can include email messages, text messages, instant messages, voicemail messages, phone calls, multimedia and/or audiovisual messages, etc.);
extracting electronic communication features of the new electronic communication (Para. [0035]-[0037]; [0044] disclosing feature extractor 206 used to identify and extract features of the incoming data including the subject of the data, recipient name, urgency, date and time);
processing, by the trained at least one machine learning computer model, the electronic communication features of the new electronic communication to generate a predicted action classification for the new electronic communication (para. [0040]-[0042], [0046] disclosing classifier using the generated pattern of past user actions analyzed by the trainer 214, may predict the user's response according to the identified pattern); and
generating an action recommendation output specifying a recommended action to take corresponding to the predicted action classification for the new electronic communication (para. [0033] and [0041] disclosing the prediction system may generate and notify user suggested predicted action to the user and await user approval of the predicted action).
Claims 2 and 12, Winn discloses the computer-implemented method of claim 1 and the computer program product of claim 11. Winn further discloses,
wherein the electronic communication features comprise one or more of an identifier of a sender of the electronic communication, an identification of one or more receivers of the electronic communication, key terms found in one of a title of the electronic communication or body content of the electronic communication, a designation of an electronic communication thread associated with the electronic communication, a timestamp of the electronic communication, or a storage location of the electronic communication (Winn para. [0044] discloses extracting sender information. Para. [0035] and [0044] “recipient name and/or list” constitutes as an identification of one or more receivers of the electronic communication. Para. [0035] “language-based content such as certain keywords or topics” is key terms found in one of a title of the electronic communication or body content of the electronic communication. Para. [0035], “other messages previously received in the same conversation” is a designation of an electronic communication thread associated with the electronic communication. Para. [0035], “date and time information,” is a timestamp of the electronic communication. Para. [0032]-[0033] disclosing save the email message to a designated folder, which constitutes a storage location of the electronic communication).
Claims 3 and 13, Winn discloses the computer-implemented method of claim 1 and the computer program product of claim 11. Winn further discloses,
determining, based on the predicted action classification for the new electronic communication, whether an automated execution of an action corresponding to the predicted action classification is to be executed; and in response to determining that an automated execution of the action corresponding to the predicted action classification is to be executed, automatically executing, by the computing device, the action (para. [0041] and claim 7 disclosing automatically execute the predicted action).
Claims 4 and 14, Winn discloses the computer-implemented method of claim 3 and the computer program product of claim 13. Winn further discloses,
wherein the action is one of forwarding the electronic communication to another user, responding to the electronic communication with a responsive electronic communication having predefined content, deleting the electronic communication, storing the electronic communication in a predefined storage location, adding the electronic communication to a watchlist data structure, or adding the electronic communication to a deferred action list data structure (para. [0073], [0078] disclosing the predicted action response may include forwarding the message. Para. [0029], [0073], and [0077] disclosing deleting the received email. Para. [0081] discloses the system can send the text message automatically containing a response by the user consistent with the predicted action response, which is responding to the electronic communication with a responsive electronic communication having predefined content. Para. [0032], [0073], discloses save the email to a folder or moving the message to a particular file folder, which is storing the electronic communication in a predefined storage location. In para. [0029] discloses “save the email for later” which is adding the electronic communication to a deferred action list data structure).
Claims 5 and 15, Winn discloses the computer-implemented method of claim 1 and the computer program product of claim 11. Winn further discloses,
wherein generating the action recommendation output comprises prioritizing the new electronic communication, relative to one or more previously received electronic communications, based on the predicted action classification for the new electronic communication and predicted action classifications associated with the one or more previously received electronic communications (para. [0080] disclosing the receiving and evaluating a plurality of income data items (electronic communications). In para. [0029], [0033], [0070], and [0082] disclose the prioritizing the email message within a list of other messages. In para. [0070], [0079] disclosing prioritizing the email includes ignore, save for later, marked as urgent or emergency. In para. [0084] discloses the messages categorized as “REPLY” are identified first on user interface view 100 because they have a temporal requirement (urgency). By comparing the temporal requirement of a new “REPLY” message against the “DELETE” classification of existing messages is generating the action recommendation for the list of prioritized new electronic communication in relative prioritization).
Claims 6 and 16, Winn discloses the computer-implemented method of claim 5 and the computer program product of claim 15. Winn further discloses,
wherein generating the action recommendation output comprises outputting a listing of the new electronic communication and the one or more previously received electronic communications, wherein the listing is ordered according to priority of the new electronic communication and the one or more previously received electronic communications (para. [0084] discloses prioritizing “REPLY” actions over “DELETE” actions based on temporal requirements. In para. [0080] discloses the generation of a user interface that displays a collection of communications (incoming data items 1010-1… 1010-N). In para. [0083] discloses resulting exemplary user interface view 1100 which displays the collection of communications. In Fig. 11 illustrates the listing of multiple messages entries. In para. [0082] discloses the system will prioritize the email notification within a list of other messages. In para. [0084] discloses the priority order, stating the message requiring “REPLY” identified first on user interface view 100 because it has higher temporal priority than “DELETE” or “FOLLOW-UP” categories).
Claims 7 and 17, Winn discloses the computer-implemented method of claim 5 and the computer program product of claim 15. Winn further discloses,
wherein the one or more previously received electronic communications are a subset of a set of previously received electronic communications, wherein the subset is selected based on a specified temporal limit (para. [0080] and [0083] disclosing the subset of current relevant data items (1010-1 through 1010-N) are presented in the user experience view 1000. In para. [0084] discloses the certain communications (1010-5 and 1010-6) may remain on user interface view 1100 for a predetermined length of time before being moved or aged out. This is example of listing of messages being prioritized is a subset defined by how recently they were received or how long they have been pending. In para. [0090] states the data items are identified and grouped “according to the time within which the user typically responds to these items,” this uses time as primary filter for active subset. In para. [0091] discloses communication is categorized and displayed in a specific priority group (“REPLY” heading) if it is received first thing in the workday morning (e.g., within a predefined time period). The predefined time period acts as the specified temporal limit for the subset).
Claims 8 and 18, Winn discloses the computer-implemented method of claim 5 and the computer program product of claim 15. Winn further discloses,
grouping the new electronic communication with another previously received electronic communication, in the one or more previously received electronic communications, having a same predicted action classification, to generate a predicted action classification group (para. [0083], [0084] Winn discloses the user interface view 1100 where data items are grouped base on predicted action. The groups are “DELETE,” “REPLY,” and “FOLLOW-UP”); and
prioritizing the predicted action classification group relative to other predicted action classification groups corresponding to other predicted action classifications, to thereby generate a first priority of the new electronic communication (Para. [0084] disclosing “REPLY” group requires timely reply and are identified first because of its temporal importance based on past practices as leaned by trainer 214 within prediction system. This is representative of “first priority”); and
prioritizing the new electronic communication relative to the other previously received electronic communication within the predicted action classification group based on content of the new electronic communication and content of the other previously received electronic communication, to generate a second priority of the new electronic communication (In para. [0070] and [0079] discloses the system analyzes the content of the message for words like “urgency” or “emergency” to elevate a message’s importance within the set of messages requires action. This is representative of “second priority”).
Claim 10, Winn discloses the computer-implemented method of claim 1. Winn further discloses,
wherein the new electronic communication is one of an electronic mail communication or an instant message communication (para. [0028]-[0029], [0032]).
Claim Rejections - 35 USC § 103
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 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.
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 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 pre-AIA 35 U.S.C. 103(a) 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 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Winn et al. (US 20130159408 A1) in view of Consul et al (US 20090319456 A1).
Claims 9 and 19, Winn discloses the computer-implemented method of claim 1 and the computer program product of claim 11. Winn further discloses,
receiving a user input in response to the action recommendation output, wherein the user input specifies whether the user agrees with the predicted action classification, disagrees with the predicted action classification, or specifies an alternative predicted action classification (Para. [0033] and [0041] discloses the system is configured to suggest the predicted action to the user and awaits user approval. In para. [0042] discloses the system monitors/observers the user action taken to determine if it coincides with the prediction for accuracy and fine tune the databased produced by the action processor in order to generate more accurate predictions);
updating the training dataset with the new electronic communication and the user input (para. [0033], [0039], [0042] disclosing the continuously and dynamically updating the data and information collected for trainer/training to adjust pre-existing data in the database of past user actions to fine tune the database’s accuracy for more accurate predictions); and
retraining the at least one machine learning computer model based on the updated training dataset (para. [0033], [0039], [0042] disclosing the continuously and dynamically updating the data and information collected for trainer/training to adjust pre-existing data in the database of past user actions to fine tune the database’s accuracy for more accurate predictions).
While Winn discloses the continuously updating and fine tuning the data of database via trainer/training process, which suggests retraining. Winn does not expressly recite the language of retraining.
Nonetheless, Consul is similar field of monitoring and handling messages with machine learning, which specifically teaches,
retraining the at least one machine learning computer model based on the updated training dataset (para. [0031], [0049], Consul teaches the performing retraining of the learning component to adapt new user tagging behavior and retraining can be triggered in response to a predetermined threshold number of automatically applied tags being overridden by the user).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filling of the invention to modify the system and method of Winn for predicting user actions using machine learning model to incorporate the feature of retraining the at least one machine learning computer model based on the updated training dataset, as taught by Consult for the motivation of ensure the reliability of providing proper tagged data and action for machine learning model of Winn’s by adapting the changes in a user’s habits (e.g., if a user suddenly starts archiving emails they previously deleted) that result few intervention from user (Consul, para. [0003]).
Relevant Prior Art Not Relied Upon
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure. The additional cited art, including but not limited to the excerpts below, further establishes the state of the art at the time of Applicant’s invention and shows the following was known:
Sundelin et al. (US 20120143798 A1) also teaches claims 2 and 12.
Jain et al. (US 20190108486 A1).
Jothilingam et al. (US 20170193349 A1).
Krishnaswamy et la. (US 20090125462 A1)
Nezhad et al. (US 20170200093 A1)
Qiu et al. (US 20200153776 A1)
Vukich et al. (US 20210344635 A1)
Mukherjee et al. “A Content-Based Approach to Email Triage Action Prediction: Exploration and Evaluation” published on April 30, 2019, Cornell University, arXiv:1905.01991.
Conclusion
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/WENREN CHEN/Primary Examiner, Art Unit 3626