Detailed Action
This action is written in response to the application filed 4/4/24. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Subject Matter Eligibility
In determining whether the claims are subject matter eligible, the examiner has considered and applied guidance from MPEP § 2106. The examiner finds that the independent claims are directed to the practical application of automatically generating insight (eg identifying anomalies, trends and correlations, as specified in dependent claim 3) using both a reinforcement learning model and a natural language model. Furthermore, the combination of steps performed in each independent claim could not be practically performed as a mental process.
Claim Rejections - 35 USC § 112(b) - Indefiniteness
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.
Claims 5/14 are rejected under 35 U.S.C. 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which applicant regards as the invention.
Claim 5/14 recite “determining, based on the reinforcement learning model, a reward as a proportion of cases leading to a true risk feedback from users over a past time period”. However, the meaning of the term “true risk feedback” is unclear; it is not a widely-used term of art within the field of machine learning and is not defined by the applicant. (The Examiner notes that the term is used in the specification at [0099], but is not defined.)
It is not clear what the ‘risk’ pertains to. The Applicant discusses a “risk profile” of a customer in their specification at [0103], but this feature and/or any related real-world application is unclaimed.
Likewise, it is unclear whether or how the ‘risk’ would apply to a reinforcement learning model.
Because it is not clear which of the above interpretations is applicable, the term is ambiguous, and consequently a person of ordinary skill would not be able to understand the scope of the claim with reasonable certainty. Therefore the claim is indefinite.
Claim Rejections - 35 USC § 102
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.
Claims 1-3, 10-12 and 19-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Burli.
(Burli, Vedavyas, and T. Satyanarayana Murthy. "Controllable and Abstractive Summarization of Clinical Trial Descriptions Using LEX-LDA Model." International Conference on Information and Management Engineering. Singapore: Springer Nature Singapore, 2022.)
Regarding claims 1, 10 and 19, Burli discloses a system, (and a related computer-implemented method and non-transitory computer readable medium) comprising:
a non-transitory memory having instructions stored thereon; and
A non-transitory memory is inherent throughout Burli.
at least one processor operatively coupled to the non-transitory memory, and configured to read the instructions to:
generate a plurality of data records based on a dataset, wherein each of the plurality of data records is generated by applying a respective combination of filters on the dataset,
P. 521, “For example, there could be missing data, noisy data, empty rows, spelling inconsistency, and applied basic filtering technique on the csv files. First we discarded all the records which did not contain the detailed trial description, calculated the length of the sentence, removed the identical title and detailed description, and retained only the rows which has longer detailed description section compared to title.”
select, using a reinforcement learning model, at least one data record from the plurality of data records based on at least one insight dimension,
P. 526, fig. 6, “Agent’s interaction with the environment results in reward”.
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P. 525, “At each step, the agent performs the action and updates the internal state representation and proceeds to the next step. Maximizing the reward and reducing the cost is the main goal of the agent (Fig. 6).”
P. 521, “Exploratory data analysis is done to get familiar with data through visualization, discover unique patterns, and identify outliers and stop words. EDA is all about making sense of data in hand. We can derive from the distribution plot that the majority of detailed description sentences lies between 0 and 50, and through the box plot, we can derive that maximum sentence count is at 300 (Fig. 3).”
generate, by applying a natural language model to each of the at least one data record, an insight data in form of human readable language based on the at least one insight dimension, and
P. 530, “Controllable Text Summarization A controllable text summarization aims to generate a summary of a text document that satisfies a specific attribute (e.g., no more than 20 words per document). Both the input document and the output summary are sequences of words, i.e., x = [x1…xlx] and y = [y1…yly], where lx and ly are words in x and y.”
transmit, for each of the at least one data record, the insight data to be presented to a user together with the respective combination of filters applied to generate the data record.
P. 529, table 2 (reproduced below), one illustrative example of an automatically generated text summary.
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Regarding claims 2 and 11, Burli discloses the further limitation wherein:
each of the plurality of data records is generated by aggregating all data points, in the dataset, falling under the respective combination of filters; and
P. 521, “For example, there could be missing data, noisy data, empty rows, spelling inconsistency, and applied basic filtering technique on the csv files. First we discarded all the records which did not contain the detailed trial description, calculated the length of the sentence, removed the identical title and detailed description, and retained only the rows which has longer detailed description section compared to title.”
different data records are generated by applying different respective combinations of filters.
The Examiner notes that this is an inherent feature of filters, as outlined above.
Regarding claims 3, 12 and 20, Burli discloses the further limitation wherein the at least one data record is selected based on:
determining, for each of the plurality of data records, a corresponding one of N insight dimensions;
The Examiner notes that this limitation is not strictly further limiting, because N may be one.
selecting, among data records corresponding to each of the N insight dimensions, M data records to identify M*N data records, wherein M and N are integers larger than one; and
P. 521, “For example, there could be missing data, noisy data, empty rows, spelling inconsistency, and applied basic filtering technique on the csv files. First we discarded all the records which did not contain the detailed trial description, calculated the length of the sentence, removed the identical title and detailed description, and retained only the rows which has longer detailed description section compared to title.”
selecting, using the reinforcement learning model, L data records from the M*N data records, wherein L is an integer larger than one and less than M*N.
Id. The Examiner notes that the passage cited describes a plurality of filters applied in series: removing records without a trial description, removing records with identical titles and descriptions, removing records with long detailed descriptions. Records are removed at each step, leaving only a subset of records from the previous step.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
The following references are relied upon in the rejections below:
Burli (Burli, Vedavyas, and T. Satyanarayana Murthy. "Controllable and Abstractive Summarization of Clinical Trial Descriptions Using LEX-LDA Model." International Conference on Information and Management Engineering. Singapore: Springer Nature Singapore, 2022.)
Goldman (US 7,810,024 B1)
Mankovskii (US 2020/0134074 A1)
Zadeh (US 2018/0204111 A1)
Claims 4 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Burli and Mankovskii.
Regarding claims 4 and 13, Mankovskii discloses the further limitation which Burli does not disclose wherein:
the N insight dimensions comprise: anomaly, trend and correlation;
anomaly :: [0021] defining outliers as “data points more than three standard deviations above a mean”.
trend :: [0077] ‘trend’
correlation :: [0041] ‘X correlates with setting Y’
a data record is determined to be an anomalous data record corresponding to an anomaly insight dimension when an isolation forest model is used to detect an abnormal value of a feature of the anomalous data record compared to the feature of other data records;
[0021] “Or in another example, a designer may associate a rule that matches to a threshold number of data points more than three standard deviations above a mean for a particular field of data over some trailing duration with a natural language text description of “outlier produced by production equipment unit XYZ.”
a data record is determined to be a trend data record corresponding to a trend insight dimension when a change point detection model is used to detect a continuous behavior change of a feature of the trend data record from a previous time period to a current time period; and
[0077] “A generalization of metrics (trend, average, max, min, etc.)”
[0038] “In some embodiments, the dashboard design record may include a plurality of records corresponding to individual data visualizations that each specify how to construct instances of the individual data visualizations. …. In some embodiments, some data visualizations may include a single field, like in a line chart showing a trendline over time, or some embodiments may include multiple fields in multidimensional data visualizations, for instance, on X and Y axes or X, Y, and Z axes or with some dimensions mapped to colors or the like. In some embodiments, the record specifying the dashboard may reference the records specifying the data visualizations therein, and in some cases, these records may be consolidated into a single record, such as a records stored in a hierarchical data serialization format, like extensible markup language or JavaScript object notation.”
a data record is determined to be a correlation data record corresponding to a correlation insight dimension when a t-test model is used to detect multiple features, of the correlation data record, which are highly correlated in a same time period compared to a threshold.
[0041] “In some embodiments, the natural language text descriptions supplied by the designer take arguments, for instance, with templates including a variable and instructions that specify how to assign a value to the variable. Examples including statements like “vibration amplitude on machine X correlates with setting Y” along with an instruction that maps the variable X to a foreign key in a table from which vibration amplitude is taken indicating an identifier of a piece of production equipment and along with an instruction maps the variable Y to another table indicating a state of settings of the machine corresponding to the value assigned X.”
At the time of filing, it would have been obvious to a skilled machine learning engineer to apply the data description techniques disclosed by Mankovskii with the system of Burli because they would provide for enhanced user understanding of identified data properties and statistics.
Claims 7-8 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Burli and Goldman.
Regarding claims 7 and 16, Goldman discloses the further limitation which Burli does not disclose wherein the at least one processor is further configured to:
perform a linearization on each of the L data records to generate L linearized data records, wherein each linearized data record includes textual tags that identify cell value and corresponding attribute of the linearized data record.
Col. 2, lines 43 et seq. “The text-based linearized tree data can include data and tags defining semantic value for the data, where the tags include element tags and attribute tags and the attribute tags include offset tags identifying the linear offset values. The metalanguage can be XML.”
At the time of filing, it would have been obvious to a skilled machine learning engineer to apply the data linearization technique disclosed by Goldman to the Burli system because this would provide for automated data intake and processing by preserving identified metadata together with the underlying data, thus facilitating later presentation to a user.
Regarding claims 8 and 17, Goldman discloses the further limitation . The system of claim 7, wherein the insight data is generated based on:
applying the natural language model to each linearized data record to generate a natural language description based on an insight dimension corresponding to the linearized data record, wherein the natural language description includes one or more interpretable insights in human readable form based on content of the linearized data record.
Col. 2, lines 43 et seq. “The text-based linearized tree data can include data and tags defining semantic value for the data, where the tags include element tags and attribute tags and the attribute tags include offset tags identifying the linear offset values. The metalanguage can be XML.”
The Examiner notes that XML tags are human readable.
Claims 9 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Burli and Zadeh.
Regarding claims 9 and 18, Zadeh discloses the further limitation which Burli does not disclose wherein the at least one processor is further configured to:
receive feedback data from a plurality of users who have viewed the insight data; and
[1329] In one embodiment, social bookmarking, tagging, page ranks, number of visitors per month, number of unique visitors per month, number of repeat visitors per month, number of new visitors per month, frequency and length of visits for a given web site or web page, number of “likes” or “dislikes” feedback for a site or topic from users, and number of links actually requested or existing for a web site, as absolute or relative numbers, or as a rate of change (first derivative) of the parameter, are all parts of the search engine analytics, for finding the more relevant search results, with respect to a specific user or general public users. In one embodiment, tagging and user comments are done as an annotation to search results, as an extra layer. In one embodiment, what other people, users, or friends have done is displayed or suggested to the user, e.g. actions performed or web sites visited or items purchased. FIG. 111 is an example of a system described above.
update one or more hyperparameters of the reinforcement learning model based on proportions of likes and dislikes in the feedback data.
Id.
See also [2125] “reinforcement learning (e.g. telling the machine if it is in the right track or not, using punishment or rewards, so that it can adjust based on an algorithm)”.
At the time of filing, it would have been obvious to a skilled machine learning engineer combine the reinforcement learning technique disclosed by Zadeh with the Burli system because this would provide for improved generative language performance using easy and intuitive feedback from users.
Additional Relevant Prior Art
The following references were identified by the Examiner as being relevant to the disclosed invention, but are not relied upon in any rejection:
Platt discloses a machine learning system for generating narratives from visualization data. (US 11,238,090 B1)
Rony discloses a system for generating text descriptions ("advanced insights") of quantitative data. See eg fig. 3. (US 11,829,705)
Sultanum discloses a machine learning system for authoring "data stories" regarding quantitative data. However, it is not prior art under sec. 102 because its priority date is after the priority date of the instant application. (2024/0362405 A1)
Claim Objections and Allowable Subject Matter
Claims 5/14 are allowable over the prior art, but are rejected under §112. None of the prior art references identified by the examiner disclose or suggest the combination of limitations recited therein, including specifically:
determining, based on the reinforcement learning model, a reward as a proportion of cases leading to a true risk feedback from users over a past time period, wherein the reward is determined based on likes and dislikes in the users’ previous feedbacks; and
selecting, using the reinforcement learning model, top L data records that the users will give a maximum proportion of likes.
Claims 6/15 are allowable over the prior art, but are rejected to as depending upon a rejected parent claim. These claims would be allowable if rewritten in independent form. None of the prior art references identified by the examiner disclose or suggest the combination of limitations recited therein, including specifically:
determining, for each of the M*N data records, a probability of selection from a corresponding fitted Beta distribution; and
selecting, using the reinforcement learning model, top L data records corresponding to top L probabilities of selection.
Conclusion
Information regarding the status of an application may be found at the USPTO Patent Center at https://patentcenter.uspto.gov. Any inquiry concerning this communication should be directed to Vincent Gonzales at (571) 270-3837. The examiner can normally be reached Monday-Friday 7 am to 4 pm MT. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Miranda Huang, can be reached at (571) 270-7092.
/Vincent Gonzales/Primary Examiner, Art Unit 2124
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