Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-15 are pending in this application.
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.
Claim 15 is rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter, software per se. The claim limitations save/store elements on “computer program product.” The claim does not recite implementing any substantive limitations on hardware elements. Accordingly, the claim is broadly interpreted as reciting pure software elements.
Claims 1-11, 13-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Independent claim 1 recites “extracting at least two respective physical data values from the physical entity data”; “determining respective numerical vectors of the respective physical data values”; “determining respective characteristic scores based on the respective numerical vectors”; “selecting characteristic physical data values from said respective physical data values, the selection being based on their respective characteristic scores”; and “wherein said extracting comprises partitioning the physical entity data into the respective physical data values”.
The limitations “extracting at least two respective physical data values from the physical entity data”; “selecting characteristic physical data values from said respective physical data values, the selection being based on their respective characteristic scores”; and “wherein said extracting comprises partitioning the physical entity data into the respective physical data values”, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process but from the recitation of implementing it on generic computer components. That is nothing in the claim element precludes the step from practically being performed in the mind. For example, “extracting” and “selecting” in the context of this claim encompasses a user determining partitions for entity data; evaluating entity data to determine data values; and then subsequently selecting characteristic values based on evaluating score data. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. The limitations “determining respective numerical vectors of the respective physical data values” and “determining respective characteristic scores based on the respective numerical vectors”, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical concept. For example, determining vectors and scores based on the vectors in the context of this claim encompasses mathematically determining numerical vectors for respective words and then plugging the vectors into an algorithm to determine scores for the entities associated with the vectors. If a claim limitation, under its broadest reasonable interpretation, covers performance mathematical concepts, then it falls within the “Mathematical Concepts” grouping of abstract ideas. Accordingly, claim 1 recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites “determining respective numerical vectors…by means of a trained neural network.” This limitation merely utilizes a general neural network as a computer tool to implement the discussed mathematical algorithms. The claim further recites “receiving physical entity data” and “returning a result comprising said respective characteristic physical data values” represent mere extra-solution activity to the judicial exception. The additional elements represent mere data gathering steps and outputting or displaying results, respectively. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim 1 is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of “trained neural network” is recited at a high level of generality and is merely a computer tool to carry out the mathematical algorithms. The other additional elements represent insignificant extra solution activity of mere data gathering and outputting/displaying results that amount to simply appending well-understood, routine, conventional activities previously known to the industry and specified at a high level of generality. According to the courts data gathering steps and outputting/displaying results do not constitute limitations that provide significant steps that amount to more than the judicial exception. Claim 1, as a whole, is directed to an abstract idea. The additional elements are not sufficient to overcome the essentially mental nature of these claims. Accordingly, claim 1 is not patent eligible.
Claims 2-15 depend on claim 1 and include all the limitations of claim 1. Therefore, claims 2-15 recite the same abstract idea practically being performed in the mind, and the analysis must therefore proceed to Step 2A Prong Two.
Claims 2-4, 7-10 recite the additional limitation “wherein the physical entity data is received in the form of a URL that relates to a website, the URL comprising at least a top-level domain and a second-level domain, and wherein said at least two respective physical data values are automatically extracted from said website;” “wherein the number of respective extracted physical data values is at least ten and wherein the physical data values are automatically extracted from a subdomain and/or from a subdirectory based on the top-level domain and the second-level domain;” “at least one of the extracted physical data values comprises a sentence present as first partition in the physical entity data; said at least one extracted physical data value being associated with a numerical vector determined based on said sentence; and wherein at least another one of the extracted physical data values comprises a media file, present as second partition in the physical entity data”; “processing the respective data values; and calculating, based on the respective processed physical data values, the respective numerical vectors; and wherein, for at least one of the physical data values, the processing comprises at least one of: tokenizing at least one word comprised in the physical data value into syllables; applying a media-to-text operation to at least one media portion comprised in the physical data value;” “wherein the respective physical data values concern respective images;” “applying a sentence encoder, to the respective text strings based on the respective physical data values;” and “tokening, with BERT tokenization, the respective text strings obtained from respective physical data values”. This judicial exception is not integrated into a practical application. The additional limitations merely indicate a field of use or technological environment in which to apply a judicial exception that does not amount to significantly more than the exception itself. The claims merely associate the mental process with a particular data source or particular type of data. This limitation is merely an incidental or token additional to the claim that does not alter or affect the mental process steps performed. Claims 2-4, 7-10 are ineligible.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements merely indicate a field of use or technological environment in which to apply a judicial exception that does not amount to significantly more than the exception itself. The claims merely limit the mental process to a particular data source or particular type of data. Claims 2-4, 7-10 are not patent eligible.
Claims 5-6 ,11 recites “determining a global numerical vector based on the respective numerical vectors; calculating a global score based on the global numerical vector, wherein said results further comprises the global score” and “applying a gradient boosting algorithm”. This judicial exception is not integrated into a practical application. The additional elements represent implementing mathematical algorithms. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mathematical Concepts” grouping of abstract ideas. This additional step is considered an abstract idea and does not integrate the judicial exception into a practical application. Accordingly, claims 5-6, 11 recite an abstract idea and is ineligible.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements represent a further mental process step. If a claim limitation, under its broadest reasonable interpretation, covers performance of mathematical algorithms, then it falls within the “Mathematical Concepts” grouping of abstract ideas. This additional step is considered an abstract idea and does not integrate the judicial exception into a practical application. An additional abstract idea is not sufficient to amount to significantly more than the judicial exception. Claims 5-6, 11 are not patent eligible.
Claims 13-15 recite the additional limitation “a processor and memory;” and “user device” and “non-transitory computer readable medium”. Implementation of the abstract idea on the hardware elements are recited at a high-level of generality. Claims 13-15 are ineligible.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception using generic computer components that cannot provide an inventive concept. Claims 13-15 are not patent eligible.
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.
Claim(s) 1, 5-6, 9, 13-15 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Bellegarda US 2020/0104369 (hereinafter Bellegarda).
For claim 1, Bellegarda teaches a computer-implemented method for detecting characteristic physical data values, the method comprising the steps of:
receiving physical entity data (see Bellegarda, [0034], “word sequence in textual form is received”);
extracting at least two respective physical data values from the physical entity data (see Bellegarda, [0034], where extracted “first word” and “second word” represent extracted physical data values);
determining respective numerical vectors of the respective physical data values by means of a trained neural network (see Bellegarda, [0031] - [0034], “plurality of forward word-level context feature vectors for the word sequence is determined. The plurality of forward word-level context feature vectors includes a first forward word-level context feature vector for the first word and a second forward word-level context feature vector for the second word after the first word” within a “neural network architecture”);
determining respective characteristic scores based on the respective numerical vectors (see Bellegarda, [0253], “process the vector representations” that “determines likelihood scores”);
selecting characteristic physical data values from said respective physical data values, the selection being based on their respective characteristic scores (see Bellegarda, [0253], [0270], [0300] – [0301], based on likelihood score, determine “respective predicted candidate sentiment” where candidate sentiment from plurality of “sentiment states” represents characteristic physical data values); and
returning a result comprising said respective characteristic physical data values (see Bellegarda, [0301] – [0302], “a result is generated” based on “determined sentiment”);
wherein said extracting comprises partitioning the physical entity data into the respective physical data values (see Bellegarda, [0252], where processing “word sequence” into “each word (or token)” represents partitioning).
For claim 5, Bellegarda teaches the method of claim 1, further comprising the steps of:
determining a global numerical vector based on the respective numerical vectors (see Bellegarda, [0034], where a “forward phrase-level feature vector” is determined based on analyzing “plurality word-level context feature vectors”); and
calculating a global score based on the global numerical vector (see Bellegarda, [0270], gathering “the pooled phrase-level feature vectors to determine likelihood scores” where likelihood score determined from a phrase-level feature vector represents a global score);
wherein said result further comprises the global score (see Bellegarda, [0034] [0270], “result is generated” based “phrase-level feature vector”).
For claim 6, Bellegarda teaches the method of claim 5, wherein the method is applied to each of said physical entity data and second physical entity data different from said physical entity data, and wherein the method comprises the further step of:
selecting one of said physical entity data and said second physical entity data based on the global score of said physical entity data and a second global score of said second physical entity data (see Bellegarda, [0253], [0270], selecting physical entity associated with “highest probability” associated with likelihood score).
For claim 9, Bellegarda teaches the method of claim 1, wherein said determining of respective numerical vectors by means of said neural network, comprises applying a sentence encoder, to respective text strings based on the respective physical data values (see Bellegarda, [0252], “Encoder 804” applied to respective text string).
For claim 13, Bellegarda teaches a device comprising a processor and memory comprising instructions which, when executed by said processor, cause the device to execute the method according to claim 1 (see Bellegarda, [0054], processor for implementing method).
For claim 14, Bellegarda teaches a system comprising the device of claim 13 and a user device comprising a display and connected to said device, wherein said device is further configured to:
receiving, from the user device, the physical entity data and/or an identification of the physical entity data (see Bellegarda, [0034], [0042], “word sequence…is received” from “user device”);
retrieving, if not received already, through downloading and/or web crawling, the physical entity data associated with said identification (see Bellegarda, [0034], physical entity data is retrieved); and
sending the result to the user device (see [0034], “generated result is outputted”); and
wherein said user device is configured to:
sending, to the device, said identification of the physical entity data; receiving, from the device, said result; and displaying said result on said display (see Bellegarda, [0042], “user device” for sending, receiving and displaying output/results).
For claim 15, Bellegarda teaches a computer program product for carrying out a computer-implemented method according to claim 1, which computer program product comprises at least one non-transitory computer readable medium in which computer-readable program code portions are saved, which program code portions comprise instructions for carrying out said method (see Bellegarda, [0193], “non-transitory computer-readable storage medium” for storing instructions to implement method).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 2-3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bellegarda US 2020/0104369 (hereinafter Bellegarda) in view of Chawla et al., US 2021/0081467 (hereinafter Chawla).
For claim 2, Chawla teaches wherein the physical entity data is received in the form of a URL that relates to a website, the URL comprising at least a top-level domain and a second-level domain, and wherein said at least two respective physical data values are automatically extracted from said website (see Chawla, [0020], [0052] “parses the URL for thew webpage” to identify “page level features” within the “website hierarchy”). It would have been obvious to one skilled in the art at the time of the invention to modify the teachings of Bellegarda (disclosing a method of vectorizing text data for scoring) with the teachings of Chawla (disclosing a method of vectoring webpage text within a website hierarchy for scoring) in order to efficiently identify content within a webpage of a website that is relevant to a user who navigates the website (see Chawla, [0019] - [0020]).
For claim 3, the combination teaches the method of claim 2, wherein the number of respective extracted physical data values is at least ten (see Bellegarda, [0034], “textual data” analyzed and vectorized contains at least 10 items), and wherein the physical data values are automatically extracted from a subdomain and/or from a subdirectory based on the top-level domain and the second-level domain (see Chawla, see Chawla, [0020], [0052], extract domain level webpage data (second-level) of website hierarchy (top level)).
Claim(s) 7-8, 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bellegarda US 2020/0104369 (hereinafter Bellegarda) in view of Malkiel et al., US 2021/0182935 (hereinafter Malkiel).
For claim 7, the combination teaches the method of claim 1, wherein the determining of the respective numerical vectors comprises the sub-steps of:
processing the respective physical data values; and
calculating, based on the respective processed physical data values, the respective numerical vectors (see Bellegarda, [0034], processing textual data to determine vectors for textual data).
Malkiel teaches wherein, for at least one of the physical data values, the processing comprises at least one of: tokenizing at least one word comprised in the physical data value into syllables; applying a media-to-text operation to at least one media portion comprised in the physical data value (see Malkiel, [0032], “text can be extracted or otherwise obtained from video or image files” represents media to text operation). It would have been obvious to one skilled in the art at the time of the invention to modify the teachings of Bellegarda with the teachings of Malkiel to extract text from analyzed content for vectorization in order to determine relevant content for a user (see Malkiel, [0023], [0032], [0035]).
For claim 8, the combination teaches the method of claim 7, wherein the respective physical data values concern respective images (see Malkiel, [0032], “image files”); wherein said processing comprises, for each image:
alt text generation, for obtaining respective text strings, wherein the calculating of the respective numerical vectors is based on said respective text strings; and/or applying a Vision Transformer, ViT, for calculating the respective numerical vectors directly (see Malkiel, [0032], “text can be extracted or otherwise obtained from video or image files” represents alt text generation).
For claim 10, Malkiel teaches wherein said determining of the numerical vectors comprises the substeps of: tokenizing, with BERT tokenization, the respective text strings obtained from respective physical data values; and calculating, with BERT encoding, based on the respective tokenized physical data values, the respective numerical vectors (see Malkiel, [0028], [0121], “Bert model” applied to “unlabeled text”). It would have been obvious to one skilled in the art at the time of the invention to modify the teachings of Bellegarda with the teachings of Malkiel to extract text from analyzed content for vectorization in order to determine relevant content for a user (see Malkiel, [0023], [0032], [0035]).
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bellegarda US 2020/0104369 (hereinafter Bellegarda) in view of Chawla et al., US 2021/0081467 (hereinafter Chawla) and further in view of Malkiel et al., US 2021/0182935 (hereinafter Malkiel).
For claim 4, the combination teaches the method of claim 3, wherein
at least one of the extracted physical data values comprises a sentence present as first partition in the physical entity data (see Bellegarda, [0033], “process the textual data with increasingly course granularity….sentence level”);
said at least one extracted physical data value being associated with a numerical vector determined based on said sentence (see Bellegarda, [0264], “phrase-level features vectors representing each sentence”); and
wherein at least another one of the extracted physical data values comprises a media file, present as second partition in the physical entity data (see Chawla, [0020], extract “images on the webpage” representing media file).
Malkiel teaches said at least another one being associated with a numerical vector determined based on a text string obtained by processing said media file with a media-to-text operation (see Malkiel, [0032], “text can be extracted or otherwise obtained from video or image files” represents media to text operation). It would have been obvious to one skilled in the art at the time of the invention to modify the teachings of Bellegarda with the teachings of Malkiel to extract text from analyzed content for vectorization in order to determine relevant content for a user (see Malkiel, [0023], [0032], [0035]).
Claim(s) 11-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bellegarda US 2020/0104369 (hereinafter Bellegarda) in view of Tam et al., US 2022/0101113 (hereinafter Tam).
For claim 11, Tam teaches wherein said determining of the respective characteristic scores based on the respective numerical vectors, involves a trained classifier applying a gradient boosting algorithm (see Tam, [0399], trained machine learning model to analyze vectorized content includes “gradient boosting”). It would have been obvious to one skilled in the art at the time of the invention to modify the teachings of Bellegarda with the teachings of Tam to apply known gradient boosting algorithms for training machine learning models to efficiently identify relevant data (see Tam, [0072], [0089], [0399]).
For claim 12, Tam teaches the method of claim 1, further comprising the step of: generating a graphical representation of said result, said graphical representation displaying the characteristic physical data values and a mark-up, wherein color and/or highlighting is indicative of a weight of respective data portions of a physical data value to the characteristic score of said physical data value (see Tam, [0100], “per-sentence passage highlighting score,” [0102], “FIG. 11 is an example passage 1050 in domain-specific data that is highlighted on a per-sentence basis using a target term,”). It would have been obvious to one skilled in the art at the time of the invention to modify the teachings of Bellegarda with the teachings of Tam to visualized relevant data that has been identified as significant (see Tam, [0102], [0108], [0387]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Misiewicz et al., US 2022/0138170. [0011], [0026].
Schwarm et al., US 2020/0026759.
Torres US 2021/0149993. [0018].
Carbune et al., US 2023/0342384.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JENSEN HU whose telephone number is (571)270-3803. The examiner can normally be reached Monday - Friday 9-5 PT.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sherief Badawi can be reached at 571-272-9782. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JENSEN HU/Primary Examiner, Art Unit 2169