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 .
Detailed Action
This action is in response to the claims filed 9/26/2023:
Claims 1 – 22 are pending.
Claims 1 and 12 are independent.
Claim Objections
Claim 10 and 21 are objected to because of the following informalities: Regarding claims 10 and 21, "specifying one the calendar events" should read "specifying one of the calendar events". Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-22 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claims 1 and 12, "values that most likely correspond to a" is indefinite. "Most likely" is a relative term of degree with no relative basis for comparison.
Regarding claims 6 and 17, claims 6 and 17 are grammatically indefinite because the phrase "using the trained machine learning classifier, having been trained using a training dataset of other calendar events, the other calendar events [...] having been labeled as internal events, to output" contains nested participial phrases that obscure the grammatical relationship among the classifier, the training dataset, the labeled calendar events, and the outputting of the predictions. The claim also appears to repeat the evaluating/classifying step of claim 1 rather than clearly limiting the previously recited trained classifier.
Regarding claims 8, 10, 19, and 21, "the calendar events that are not associated with the one or more activities" lacks antecedent basis.
The remaining claims are rejected with respect to their dependence on the rejected claims.
Claim Rejections - 35 USC § 101
101 Rejection
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 1-22 are rejected under 35 USC § 101 because the claimed invention is directed to non-statutory subject matter.
Regarding Claim 1: Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 1 is directed to an method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: Claim 1 under its broadest reasonable interpretation is a series of mental processes. For example, but for the generic computer components language, the above limitations in the context of this claim encompass machine learning processing, including the following:
processing one or more of the one or more first calendar events to extract organization-independent features, yielding one or more processed calendar events (observation, evaluation, and judgement),
evaluating each of the one or more processed calendar events using a […] classifier to output a prediction of whether each of the one or more processed calendar events represents an internal event of an entity, and classifying the one or more processed calendar events having prediction values less than a threshold prediction value as activity events (observation, evaluation, and judgement)
to embed the activity events as target vectors and to embed a corpus based on the one or more first calendar events and user account records as candidate vectors (observation, evaluation, and judgement)
executing a similarity search of the target vectors against the candidate vectors to output a set of linking candidate vector values that most likely correspond to a particular activity that is represented in the corpus (observation, evaluation, and judgement)
ranking the set of linking candidate vector values to form a set of ranked candidate vector values (observation, evaluation, and judgement)
Therefore, claim 1 recites an abstract idea which is a judicial exception.
Step 2A Prong Two Analysis: Claim 1 recites additional elements “using a trained machine learning classifier”, “executing a transformer-based machine learning model”. However, these additional features are computer components recited at a high-level of generality, such that they amount to no more than mere instructions to apply the judicial exception using a generic computer component. An additional element that merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, does not integrate the judicial exception into a practical application (See MPEP 2106.05(f)). Claim 1 also recites additional elements “transmitting one or more database queries to a database to cause executing searches that return result sets of one or more first calendar events, one or more linked second calendar events that are associated in the database with one or more activities, and one or more user account records that specify associations of user accounts to the one or more activities” and “transmitting, to a second computer, presentation instructions that are programmed to cause the second computer to display one or more of the activity events that correspond to the set of ranked candidate vector values in a graphical user interface” which amounts to gathering and outputting data which is insignificant extra-solution activity (See MPEP 2106.05(g)). Therefore, claim 1 is directed to a judicial exception.
Step 2B Analysis: Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the lack of integration of the abstract idea into a practical application, the additional elements recited in claim 1 amount to no more than mere instructions to apply the judicial exception using a generic computer component and insignificant extra-solution activity. The gathering and outputting data is considered well-understood, routine, and conventional in the art (See MPEP 2106.05(d)(II)(i)).
For the reasons above, claim 1 is rejected as being directed to non-patentable subject matter under §101. This rejection applies equally to independent claim 12, which recites a computer readable media, as well as to dependent claims 2-11 and 13-22.
Independent claim 12 recites additional instructions to apply the judicial exception using generic computer components “One or more non-transitory computer-readable data storage media storing one or more sequences of instructions which, when executed using one or more processors of a first computer, cause the one or more processors to execute:”.
The additional limitations of the dependent claims are addressed briefly below:
Dependent claims 2 and 13 recite additional observation, evaluation, and judgement “evaluating each of the one or more processed calendar events […] to output the prediction of whether each of the one or more processed calendar events represents the internal event of the entity, then performing the classifying the one or more processed calendar events based on the prediction” as well as additional instructions to apply the judicial exception using generic computer components “using a trained naïve Bayes machine learning classifier”
Dependent claims 3 and 14 recite additional observation, evaluation, and judgement “to embed the activity events as the target vectors and to embed the corpus based on the one or more first calendar events and the user account records as the candidate vectors” as well as additional instructions to apply the judicial exception using generic computer components “executing a Sentence-BERT transformer-based machine learning model”
Dependent claims 4 and 15 recite additional observation, evaluation, and judgement “executing the similarity search as a nearest neighbor search of the target vectors against the candidate vectors to output the set of linking candidate vector values”
Dependent claims 5 and 16 recite additional observation, evaluation, and judgement “ranking the set of linking candidate vector values to form the set of ranked candidate vector values” as well as additional instructions to apply the judicial exception using generic computer components “executing a gradient boosted tree algorithm”
Dependent claims 6 and 17 recite additional insignificant extra-solution activity “evaluating each of the one or more processed calendar events […] to output a prediction of whether each of the one or more processed calendar events represents an internal event of an entity, and then performing the classifying based on the prediction” As well as additional instructions to apply the judicial exception using generic computer components “using the trained machine learning classifier, having been trained using a training dataset of other calendar events, the other calendar events that are not linked to the one or more activities having been labeled as internal events”
Dependent claims 7 and 18 recite additional insignificant extra-solution activity of gathering and outputting data (See MPEP 2106.05(g)) “receiving, from the second computer, input signaling a selection of a particular activity event from among the one or more of the activity events in the graphical user interface, and in response to the input, creating and storing in the database an association of the particular activity event to the particular activity” which is well-understood, routine, and conventional in the art (see MPEP 2106.05(d)(II)(iv))
Dependent claims 8 and 19 recite additional insignificant extra-solution activity of gathering and outputting data (See MPEP 2106.05(g)) “transmitting, to the second computer, presentation instructions that are programmed to cause the second computer to display an electronic calendar graphical user interface comprising a list of a plurality of the calendar events that are not associated with the one or more activities; receiving, from the second computer, first input signaling a selection of a particular calendar event in the list of the plurality of the calendar events that are not associated with the one or more activities; transmitting, to the second computer, presentation instructions that are programmed to cause the second computer to display a graphical panel specifying one or more of the activity events that correspond to the set of the ranked candidate vector values” which is well-understood, routine, and conventional in the art (see MPEP 2106.05(d)(II)(iv))
Dependent claims 9 and 20 recite additional insignificant extra-solution activity of gathering and outputting data (See MPEP 2106.05(g)) “receiving, from the second computer, second input signaling another selection of a particular activity event from among the one or more of the activity events in the graphical panel; in response to the second input, creating and storing in the database an association of the particular activity event to the particular activity” which is well-understood, routine, and conventional in the art (see MPEP 2106.05(d)(II)(iv))
Dependent claims 10 and 21 recite additional insignificant extra-solution activity of gathering and outputting data (See MPEP 2106.05(g)) “transmitting, to the second computer, presentation instructions that are programmed to cause the second computer to display an electronic calendar graphical user interface comprising at least one first graphical panel associated with a calendar date and a time of day and specifying one the calendar events that is not associated with the one or more activities; receiving, from the second computer, first input signaling a selection of a particular first graphical panel; transmitting, to the second computer, presentation instructions that are programmed to cause the second computer to display a second graphical panel specifying one or more of the activity events that correspond to the ranked candidate vector values.” which is well-understood, routine, and conventional in the art (see MPEP 2106.05(d)(II)(iv))
Dependent claims 11 and 22 recite additional insignificant extra-solution activity of gathering and outputting data (See MPEP 2106.05(g)) “receiving, from the second computer, second input signaling another selection of a particular activity event from among the one or more of the activity events in the second graphical panel; in response to the second input, creating and storing in the database an association of the particular activity event to the particular activity.” which is well-understood, routine, and conventional in the art (see MPEP 2106.05(d)(II)(iv))
Therefore, when considering the elements separately and in combination, they do not add significantly more to the inventive concept. Accordingly, claims 1-22 are rejected under 35 U.S.C. § 101.
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 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, 3, 4, 6, 7, 12, 14, 15, 17, and 18 are rejected under U.S.C. §103 as being unpatentable over the combination of Rogynskyy (US20190361910A1) and Ortiz (US20220301013A1).
Regarding claim 1, Rogynskyy teaches A computer-implemented method executed using a first computer and comprising:([¶0535] "FIG. 20 shows a simplified block diagram of a representative server system 2000 and client computer system 2014" Server system 2000 interpreted as first computer)
transmitting one or more database queries to a database to cause executing searches that return result sets of one or more first calendar events, ([¶0481] "The data source provider 9350 can store electronic activity 9305(1)—electronic activity 9305(N) (generally referred to as electronic activity 9305) in the data source 9355. As described above, the electronic activities can include one or more forms of electronic activity, such as email or other forms of electronic communication. The data processing system 9300 can access or otherwise retrieve the electronic activity 9305 from the data source 9355." [¶0463] "electronic activities can include [...] electronic calendar events")
one or more linked second calendar events that are associated in the database with one or more activities,([¶0221] "the system can scan one or more systems of record that include manually matched electronic activity and record objects" [¶0482] "The data processing system 9300 can match the electronic activities 9305 to one or more record objects 1602 of the shadow system of record 9330. The record objects 1602 of the shadow system of record 9330 can be synced with the record object 1602 of the system of record 9360. Syncing the shadow record objects 1602 with the record objects 1602 of the system of record 9360 can include adding values from fields of the shadow record objects 1602 to the corresponding values, such as matched electronic activities 9305, of the record objects 1602 in the system of record 9360." [¶0463] "electronic activities can include [...] electronic calendar events" [¶0532] "the data processing system 9300 can identify subsequent electronic activities that are related to the matched electronic activities" Rogynskyy explicitly stores associations between electronic activities and record objects and identifies related (second) electronic activities after a first activity is matched)
and one or more user account records that specify associations of user accounts to the one or more activities;([¶0478] "For an electronic activity that is eligible or qualifies to be matched with one or more record objects, the system can identify one or more set of rules or rule sets. Using the rule sets, the system can identify candidate record objects." Rogynskyy explicitly stores associations between electronic activities and record objects)
processing one or more of the one or more first calendar events to extract organization-independent features, yielding one or more processed calendar events;([¶0198] " The electronic activity linking engine 250 can include a feature extraction engine 310 to extract features from the electronic activities that can be used to link electronic activities with one or more record objects of systems of records" [¶0200] "generate corresponding feature vectors for the one or more electronic activities." [¶0201] "parse electronic activities and extract features from electronic activities")
evaluating each of the one or more processed calendar events using a trained machine learning classifier([¶0219] "the matching model 340 used to link electronic activities to one or more record objects can be trained using machine learning")
to output a prediction of whether each of the one or more processed calendar events represents an internal event of an entity, ([¶0116] "The tagging engine 265 can assign one or more tags to electronic activities. The tagging engine 265 can determine […] if the meeting or electronic message is internal or external and can assign an internal tag to meetings or emails identified as internal or an external tag to meetings and emails identified as external" [¶0323] "The machine learning filtering technique can include bot detection. The system 200 (e.g., electronic activity parser 210, tagging engine 265 or filtering engine 270) can use or be configured with a bot detection machine learning algorithm" See FIG. 4)
and classifying the one or more processed calendar events having prediction values less than a threshold prediction value as activity events;([¶0326] "the tagging engine 265 can use natural language processing or machine learning techniques to automatically assess sensitivity of data from a new sender. For example, if a new source starts sending emails to multiple users, and greater than a threshold percentage (e.g., 70%, 80%, 90% or some other percentage) of the emails contain sensitive or confidential information (e.g., social security numbers), then the system 200 can automatically generate and apply a global filter to automatically blacklist this source")
executing a [transformer-based] machine learning model to embed the activity events as target vectors and to embed a corpus based on the one or more first calendar events and user account records as candidate vectors;([¶0003] "The plurality of data sources can further include systems of record, such as customer relationship management systems, enterprise resource planning systems, document management systems, applicant tracking systems or other sources of data that may maintain electronic activities, activities or records." [¶0455] "one or more systems of record" [¶0322] "the electronic activities are converted into vectors with word2vec or similar technology, a machine learning model can be trained based on the content of the electronic activities alongside (not mutually exclusive) with natural language processing systems" [¶0450] "As described above with respect to matching electronic activities to record objects, the system 200 can be configured to identify candidate record objects to match electronic activities based on account teams" System of record interpreted as corpus based on the one or more first calendar events and user account records)
executing a similarity search of the target vectors against the candidate vectors to output a set of linking candidate vector values that most likely correspond to a particular activity that is represented in the corpus;([¶0219] "The matching model 340 can use neural networks, nearest neighbor classification, or other modeling approaches to classify the electronic activity based on the feature vector." [¶0220] "the electronic activity to a record object based on a similarity of the text in and the sender of the electronic activity with the text in and sender of an electronic activity previously matched to a given electronic activity" [¶0129] "responsive to determining that a similar electronic activity that is similar to the instant electronic activity above a predetermined similarity threshold was associated to a large number of nodes in a node storage database maintained by the system 200.")
ranking the set of linking candidate vector values to form a set of ranked candidate vector values;([¶0522] "Each of the matching rules can have a priority level, score, or weight. The candidate record objects selected with rules with a higher priority level can be assigned a higher score" [¶0322] "the electronic activities are converted into vectors with word2vec or similar technology, a machine learning model can be trained based on the content of the electronic activities alongside (not mutually exclusive) with natural language processing systems" [¶0478] "For an electronic activity that is eligible or qualifies to be matched with one or more record objects, the system can identify one or more set of rules or rule sets. Using the rule sets, the system can identify candidate record objects.")
transmitting, to a second computer, presentation instructions that are programmed to cause the second computer to display one or more of the activity events that correspond to the set of ranked candidate vector values in a graphical user interface.([¶0535] "FIG. 20 shows a simplified block diagram of a representative server system 2000 and client computer system 2014" [¶0546] "client computing system 2014 can communicate via WAN interface 2010" [¶0550] "User output device 2024 can include any device via which client computing system 2014 can provide information to a user. For example, user output device 2024 can include a display to display images generated by or delivered to client computing system 2014" See FIG. 20 client computer system 2014 interpreted as second computer and see user output 2024).
However, Rogynskyy does not explicitly teach executing a transformer-based machine learning model to embed the activity events as target vectors and to embed a corpus based on the one or more first calendar events and user account records as candidate vectors;.
Ortiz, in the same field of endeavor, teaches executing a transformer-based machine learning model to embed the activity events as target vectors and to embed a corpus based on the one or more first calendar events and user account records as candidate vectors; ([¶0111] "For text embedding, Applicants have experimented with many embedding algorithms such as TFIDF, Word2Vec, Doc2vec, and Bert based sentence transformers (SBert)" [¶0015] "Personalizations can further include recommending various financial products (e.g., using a specific card, loans, pay by bank, buy now pay later) based on the tracked data linkages. In some embodiments, personalizations may also interface elements rendered based on an impact on a user's creditworthiness or various financial metrics, such as an income to spend ratios, debt/income calendars, loyalty point trackers, among others (e.g., a financial “genie” noting whether a purchase is in accordance with specific financial outcomes). Personalizations may be represented by data objects that may be generated on a tailored basis, or generated in batch and provided to intent tracker engine 102").
Rogynskyy as well as Ortiz are directed towards machine learning calendar based vector filtering. Therefore, Rogynskyy as well as Ortiz are analogous art in the same field of endeavor. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of Rogynskyy with the teachings of Ortiz by using a transformer model to execute the system in Rogynskyy. Ortiz provides as additional motivation for combination that the transformer is operable to ([¶0004] “to improve the relevance of offers surfaced to the user, in particular, based on machine learning-based representations of the user's intent, actual purchases, and data linkages maintained there between.”).
Regarding claim 3, the combination of Rogynskyy and Ortiz teaches The computer-implemented method of claim 1, further comprising executing a Sentence-BERT transformer-based machine learning model to embed the activity events as the target vectors and to embed the corpus based on the one or more first calendar events and the user account records as the candidate vectors. (Ortiz [¶0111] "For text embedding, Applicants have experimented with many embedding algorithms such as TFIDF, Word2Vec, Doc2vec, and Bert based sentence transformers (SBert)" [¶0015] "Personalizations can further include recommending various financial products (e.g., using a specific card, loans, pay by bank, buy now pay later) based on the tracked data linkages. In some embodiments, personalizations may also interface elements rendered based on an impact on a user's creditworthiness or various financial metrics, such as an income to spend ratios, debt/income calendars, loyalty point trackers, among others (e.g., a financial “genie” noting whether a purchase is in accordance with specific financial outcomes). Personalizations may be represented by data objects that may be generated on a tailored basis, or generated in batch and provided to intent tracker engine 102").
Regarding claim 4, the combination of Rogynskyy and Ortiz teaches The computer-implemented method of claim 1, further comprising executing the similarity search as a nearest neighbor search of the target vectors against the candidate vectors to output the set of linking candidate vector values. (Rogynskyy [¶0219] " the features extraction engine 310 can generate a feature vector for each electronic activity. The matching model 340 can use neural networks, nearest neighbor classification").
Regarding claim 6, the combination of Rogynskyy and Ortiz teaches The computer-implemented method of claim 1, further comprising evaluating each of the one or more processed calendar events using the trained machine learning classifier, having been trained using a training dataset of other calendar events, the other calendar events that are not linked to the one or more activities having been labeled as internal events, to output a prediction of whether each of the one or more processed calendar events represents an internal event of an entity, and then performing the classifying based on the prediction. (Rogynskyy [¶0219] "the matching model 340 used to link electronic activities to one or more record objects can be trained using machine learning" [¶0116] "The tagging engine 265 can assign one or more tags to electronic activities. The tagging engine 265 can determine […] if the meeting or electronic message is internal or external and can assign an internal tag to meetings or emails identified as internal or an external tag to meetings and emails identified as external" [¶0323] "The machine learning filtering technique can include bot detection. The system 200 (e.g., electronic activity parser 210, tagging engine 265 or filtering engine 270) can use or be configured with a bot detection machine learning algorithm").
Regarding claim 7, the combination of Rogynskyy and Ortiz teaches The computer-implemented method of claim 1, further comprising receiving, from the second computer, input signaling a selection of a particular activity event from among the one or more of the activity events in the graphical user interface, (Rogynskyy [¶0239] " the system can request input from the user as to which record object to match the electronic activity. In these cases, the matching strategies can be updated based on the input from the user." [¶0535] "FIG. 20 shows a simplified block diagram of a representative server system 2000 and client computer system 2014" [¶0546] "client computing system 2014 can communicate via WAN interface 2010" [¶0550] "User output device 2024 can include any device via which client computing system 2014 can provide information to a user. For example, user output device 2024 can include a display to display images generated by or delivered to client computing system 2014" See FIG. 20)
and in response to the input, creating and storing in the database an association of the particular activity event to the particular activity. (Rogynskyy [¶0239] " the system can request input from the user as to which record object to match the electronic activity. In these cases, the matching strategies can be updated based on the input from the user." [¶0531] "the data processing system 9300 can store, in a data structure, an association between the selected candidate record objects and the electronic activity").
Regarding claims 12, 14-15, and 17-18, claims 12, 14-15, and 17-18 are directed towards a computer readable media for performing the method of claims 1, 3-4, and 6-7. Therefore, the rejection applied to claims 1, 3-4, and 6-7 also apply to claims 12, 14-15, and 17-18. Claim 12 recites additional elements One or more non-transitory computer-readable data storage media storing one or more sequences of instructions which, when executed using one or more processors of a first computer, cause the one or more processors to execute: (Rogynskyy [¶0551] "Many of the features described in this specification can be implemented as processes that are specified as a set of program instructions encoded on a computer readable storage medium. When these program instructions are executed by one or more processing units, they cause the processing unit(s) to perform various operation indicated in the program instructions.").
Claims 2, 5, 13, and 16 are rejected under U.S.C. §103 as being unpatentable over the combination of Rogynskyy and Ortiz and in further view of Sourav (“DEEP LEARNING BASED ANDROID MALWARE DETECTION FRAMEWORK”, 2019).
Regarding claim 2, the combination of Rogynskyy and Ortiz teaches The computer-implemented method of claim 1.
However, the combination of Rogynskyy and Ortiz doesn't explicitly teach further comprising evaluating each of the one or more processed calendar events using a trained naïve Bayes machine learning classifier to output the prediction of whether each of the one or more processed calendar events represents the internal event of the entity, then performing the classifying the one or more processed calendar events based on the prediction.
Sourav, in the same field of endeavor, teaches evaluating each of the one or more processed calendar events using a trained naïve Bayes machine learning classifier to output the prediction of whether each of the one or more processed calendar events represents the internal event of the entity, then performing the classifying the one or more processed calendar events based on the prediction. ([p. 3] "Table 1: Overview of the features extracted from the apps. […] android.permission.WRITE-CALENDAR" [p. 4 §3.2] "Second part involves training the machine learning models using the processed dataset and saving weights and the trained model. We use Logistic Regression, Naive Bayes and Gradient Boosted Trees along with the artificial neural network and compare their performance on the test data").
The combination of Rogynskyy and Ortiz as well as Sourav are directed towards machine learning calendar aware vector filtering. Therefore, the combination of Rogynskyy and Ortiz as well as Sourav are analogous art in the same field of endeavor. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of the combination of Rogynskyy and Ortiz with the teachings of Sourav by using Naïve Bayes or Gradient Boosting Trees in the filtering system. Sourav provides as additional motivation for combination that this combination ([p. 5 §5] “performs significantly better than traditional off the shelf machine learning techniques”).
Regarding claim 5, the combination of Rogynskyy and Ortiz teaches The computer-implemented method of claim 1.
However, the combination of Rogynskyy and Ortiz doesn't explicitly teach, further comprising ranking the set of linking candidate vector values to form the set of ranked candidate vector values by executing a gradient boosted tree algorithm.
Sourav, in the same field of endeavor, teaches ranking the set of linking candidate vector values to form the set of ranked candidate vector values by executing a gradient boosted tree algorithm. ([p. 3] "Table 1: Overview of the features extracted from the apps. […] android.permission.WRITE-CALENDAR" [p. 4 §3.2] "Second part involves training the machine learning models using the processed dataset and saving weights and the trained model. We use Logistic Regression, Naive Bayes and Gradient Boosted Trees along with the artificial neural network and compare their performance on the test data" [p. 1] "Trojan-SMS malware (6.83%) ranked second in terms of the growth rate").
The combination of Rogynskyy and Ortiz as well as Sourav are directed towards machine learning calendar aware vector filtering. Therefore, the combination of Rogynskyy and Ortiz as well as Sourav are analogous art in the same field of endeavor. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of the combination of Rogynskyy and Ortiz with the teachings of Sourav by using Naïve Bayes or Gradient Boosting Trees in the filtering system. Sourav provides as additional motivation for combination that this combination ([p. 5 §5] “performs significantly better than traditional off the shelf machine learning techniques”).
Regarding claims 13 and 16, claims 13 and 16 are directed towards a computer readable media for performing the methods of claims 2 and 5, respectively. Therefore, the rejections applied to claims 2 and 5 also apply to claims 13 and 16.
Claims 8-11 and 19-22 are rejected under U.S.C. §103 as being unpatentable over the combination of Rogynskyy and Ortiz and in further view of Bennett (US20190236555A1).
Regarding claim 8, the combination of Rogynskyy and Ortiz teaches The computer-implemented method of claim 1.
However, the combination of Rogynskyy and Ortiz doesn't explicitly teach, further comprising: transmitting, to the second computer, presentation instructions that are programmed to cause the second computer to display an electronic calendar graphical user interface comprising a list of a plurality of the calendar events that are not associated with the one or more activities;
receiving, from the second computer, first input signaling a selection of a particular calendar event in the list of the plurality of the calendar events that are not associated with the one or more activities;
transmitting, to the second computer, presentation instructions that are programmed to cause the second computer to display a graphical panel specifying one or more of the activity events that correspond to the set of the ranked candidate vector values.
Bennett, in the same field of endeavor, teaches transmitting, to the second computer, presentation instructions that are programmed to cause the second computer to display an electronic calendar graphical user interface comprising a list of a plurality of the calendar events that are not associated with the one or more activities; ([¶0037] " The PIM 102 may provide a list of the top-k items in a recommended item interface 110 to the user. The recommended item interface 110 may include a description of each of the items and a link, that when selected by a user, provides a user a view of the item.")
receiving, from the second computer, first input signaling a selection of a particular calendar event in the list of the plurality of the calendar events that are not associated with the one or more activities; ([¶0041] "One panel may list the upcoming calendar events of the user (e.g., ordered by time, such as in the calendar events 206) and another panel may present a list of resource recommendations for the calendar event selected in the other panel (e.g., in the useful resources 202)")
transmitting, to the second computer, presentation instructions that are programmed to cause the second computer to display a graphical panel specifying one or more of the activity events that correspond to the set of the ranked candidate vector values. ([¶0037] " The PIM 102 may provide a list of the top-k items in a recommended item interface 110 to the user. The recommended item interface 110 may include a description of each of the items and a link, that when selected by a user, provides a user a view of the item.").
The combination of Rogynskyy and Ortiz as well as Bennett are directed towards calendar aware resource retrieval. Therefore, the combination of Rogynskyy and Ortiz as well as Bennett are analogous art in the same field of endeavor. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of the combination of Rogynskyy and Ortiz with the teachings of Bennett by using the interface in Bennett to provide the user feedback for the system in Rogynskyy. Bennett provides as additional motivation for combination ([¶0028] “The calendar interface 106 provides controls for a user to record data regarding a date, time, people attending, subject, attachment, a description of an event (e.g., a body), a reminder time, or the like.”).
Regarding claim 9, the combination of Rogynskyy, Ortiz, and Bennett teaches The computer-implemented method of claim 8, further comprising: receiving, from the second computer, second input signaling another selection of a particular activity event from among the one or more of the activity events in the graphical panel;(Rogynskyy [¶0239] " the system can request input from the user as to which record object to match the electronic activity. In these cases, the matching strategies can be updated based on the input from the user." [¶0535] "FIG. 20 shows a simplified block diagram of a representative server system 2000 and client computer system 2014" [¶0546] "client computing system 2014 can communicate via WAN interface 2010" [¶0550] "User output device 2024 can include any device via which client computing system 2014 can provide information to a user. For example, user output device 2024 can include a display to display images generated by or delivered to client computing system 2014" See FIG. 20)
in response to the second input, creating and storing in the database an association of the particular activity event to the particular activity.(Rogynskyy [¶0239] " the system can request input from the user as to which record object to match the electronic activity. In these cases, the matching strategies can be updated based on the input from the user." [¶0531] "the data processing system 9300 can store, in a data structure, an association between the selected candidate record objects and the electronic activity").
Regarding claim 10, the combination of Rogynskyy and Ortiz teaches The computer-implemented method of claim 1.
However, the combination of Rogynskyy and Ortiz doesn't explicitly teach, further comprising: transmitting, to the second computer, presentation instructions that are programmed to cause the second computer to display an electronic calendar graphical user interface comprising at least one first graphical panel associated with a calendar date and a time of day and specifying one the calendar events that is not associated with the one or more activities;
receiving, from the second computer, first input signaling a selection of a particular first graphical panel;
transmitting, to the second computer, presentation instructions that are programmed to cause the second computer to display a second graphical panel specifying one or more of the activity events that correspond to the ranked candidate vector values..
Bennett, in the same field of endeavor, teaches transmitting, to the second computer, presentation instructions that are programmed to cause the second computer to display an electronic calendar graphical user interface comprising at least one first graphical panel associated with a calendar date and a time of day and specifying one the calendar events that is not associated with the one or more activities;([¶0037] " The PIM 102 may provide a list of the top-k items in a recommended item interface 110 to the user. The recommended item interface 110 may include a description of each of the items and a link, that when selected by a user, provides a user a view of the item.")
receiving, from the second computer, first input signaling a selection of a particular first graphical panel; ([¶0041] "One panel may list the upcoming calendar events of the user (e.g., ordered by time, such as in the calendar events 206) and another panel may present a list of resource recommendations for the calendar event selected in the other panel (e.g., in the useful resources 202)")
transmitting, to the second computer, presentation instructions that are programmed to cause the second computer to display a second graphical panel specifying one or more of the activity events that correspond to the ranked candidate vector values. ([¶0037] " The PIM 102 may provide a list of the top-k items in a recommended item interface 110 to the user. The recommended item interface 110 may include a description of each of the items and a link, that when selected by a user, provides a user a view of the item.").
The combination of Rogynskyy and Ortiz as well as Bennett are directed towards calendar aware resource retrieval. Therefore, the combination of Rogynskyy and Ortiz as well as Bennett are analogous art in the same field of endeavor. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of the combination of Rogynskyy and Ortiz with the teachings of Bennett by using the interface in Bennett to provide the user feedback for the system in Rogynskyy. Bennett provides as additional motivation for combination ([¶0028] “The calendar interface 106 provides controls for a user to record data regarding a date, time, people attending, subject, attachment, a description of an event (e.g., a body), a reminder time, or the like.”).
Regarding claim 11, the combination of Rogynskyy, Ortiz, and Bennett teaches The computer-implemented method of claim 10, further comprising: receiving, from the second computer, second input signaling another selection of a particular activity event from among the one or more of the activity events in the second graphical panel;(Rogynskyy [¶0239] " the system can request input from the user as to which record object to match the electronic activity. In these cases, the matching strategies can be updated based on the input from the user." [¶0535] "FIG. 20 shows a simplified block diagram of a representative server system 2000 and client computer system 2014" [¶0546] "client computing system 2014 can communicate via WAN interface 2010" [¶0550] "User output device 2024 can include any device via which client computing system 2014 can provide information to a user. For example, user output device 2024 can include a display to display images generated by or delivered to client computing system 2014" See FIG. 20)
in response to the second input, creating and storing in the database an association of the particular activity event to the particular activity.(Rogynskyy [¶0239] " the system can request input from the user as to which record object to match the electronic activity. In these cases, the matching strategies can be updated based on the input from the user." [¶0531] "the data processing system 9300 can store, in a data structure, an association between the selected candidate record objects and the electronic activity").
Regarding claims 19-22, claims 19-22 are directed towards a computer readable media for performing the methods of claims 8-11, respectively. Therefore, the rejections applied to claims 8-11 also apply to claims 19-22.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Almasian (“BERT got a Date: Introducing Transformers to Temporal Tagging”, 2021) is directed towards a calendar aware transformer system.
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/SIDNEY VINCENT BOSTWICK/Examiner, Art Unit 2124
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