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
Status of the Application
The following is a Final Office Action in response to communication received on 12/22/2025. Claims 1-7 and 9-21 are pending in this office action.
Response to Amendment
Applicant’s amendments to claims 1-4,6-7, 9, 11-13, 15, 17, and 21 are acknowledged. Applicant’s cancellation of claim 8 is acknowledged.
Response to Arguments
On Remarks pages 10-11, Applicant argues the 101 rejection. Specifically here Applicant argues claim 1 as amended. Here Applicant argues there is an interaction of devices and not merely steps performed at a single computer. Therefore the steps are far more than mental steps. The Examiner respectfully disagrees.
Claim 1 is still recited at such a high level of generality that it recites mental process and certain method of organizing human activities. Specifically claim 1 recites collecting data, identifying reviews, categorizing the reviews as positive or negative based on words describing the specific aspect like packaging, determining whether the reviews meet an assurance level and providing results. The additional elements that the information is from a “website” and limitations are being performed using a “computer” based system merely results in “apply it.”
Specifically the claim invokes a computer or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g. to receive, store or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea does not integrate a judicial exception into a practical application or provide significantly more.
Further limitations that could be gathered by humans or humans for example paper reviews etc. that instead recite being gathered from a website and that recite these are instead performed using a “computer” based system merely generally link the use of the judicial exception to the field of computers.
Further as to Applicant’s arguments that there is interaction between devices and not a single computer, and therefore not a mental process, the Examiner respectfully disagrees. For one, Applicant merely recites a “computer-based” system, there is no recitation in claim 1 of multiple computers interacting as argued by Applicant therefore the Examiner respectfully disagrees. Further even if that was the case, merely having multiple computers perform the mental process or human activities through a computer based evaluation system would be merely performing a mental process in a computer environment and using a computer as a tool to perform a mental process, which are situations that the claim is still considered to recite a mental process, See MPEP 2106.04(a), cited herein:
In evaluating whether a claim that requires a computer recites a mental process, examiners should carefully consider the broadest reasonable interpretation of the claim in light of the specification. For instance, examiners should review the specification to determine if the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept. In these situations, the claim is considered to recite a mental process.
1. Performing a mental process on a generic computer. An example of a case identifying a mental process performed on a generic computer as an abstract idea is Voter Verified, Inc. v. Election Systems & Software, LLC, 887 F.3d 1376, 1385, 126 USPQ2d 1498, 1504 (Fed. Cir. 2018). In this case, the Federal Circuit relied upon the specification in explaining that the claimed steps of voting, verifying the vote, and submitting the vote for tabulation are "human cognitive actions" that humans have performed for hundreds of years. The claims therefore recited an abstract idea, despite the fact that the claimed voting steps were performed on a computer. 887 F.3d at 1385, 126 USPQ2d at 1504. Another example is Versata, in which the patentee claimed a system and method for determining a price of a product offered to a purchasing organization that was implemented using general purpose computer hardware. 793 F.3d at 1312-13, 1331, 115 USPQ2d at 1685, 1699. The Federal Circuit acknowledged that the claims were performed on a generic computer, but still described the claims as "directed to the abstract idea of determining a price, using organizational and product group hierarchies, in the same way that the claims in Alice were directed to the abstract idea of intermediated settlement, and the claims in Bilski were directed to the abstract idea of risk hedging." 793 F.3d at 1333; 115 USPQ2d at 1700-01.
2. Performing a mental process in a computer environment. An example of a case identifying a mental process performed in a computer environment as an abstract idea is Symantec Corp., 838 F.3d at 1316-18, 120 USPQ2d at 1360. In this case, the Federal Circuit relied upon the specification when explaining that the claimed electronic post office, which recited limitations describing how the system would receive, screen and distribute email on a computer network, was analogous to how a person decides whether to read or dispose of a particular piece of mail and that "with the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper". 838 F.3d at 1318, 120 USPQ2d at 1360. Another example is FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 120 USPQ2d 1293 (Fed. Cir. 2016). The patentee in FairWarning claimed a system and method of detecting fraud and/or misuse in a computer environment, in which information regarding accesses of a patient’s personal health information was analyzed according to one of several rules (i.e., related to accesses in excess of a specific volume, accesses during a pre-determined time interval, or accesses by a specific user) to determine if the activity indicates improper access. 839 F.3d. at 1092, 120 USPQ2d at 1294. The court determined that these claims were directed to a mental process of detecting misuse, and that the claimed rules here were "the same questions (though perhaps phrased with different words) that humans in analogous situations detecting fraud have asked for decades, if not centuries." 839 F.3d. at 1094-95, 120 USPQ2d at 1296.
3. Using a computer as a tool to perform a mental process. An example of a case in which a computer was used as a tool to perform a mental process is Mortgage Grader, 811 F.3d. at 1324, 117 USPQ2d at 1699. The patentee in Mortgage Grader claimed a computer-implemented system for enabling borrowers to anonymously shop for loan packages offered by a plurality of lenders, comprising a database that stores loan package data from the lenders, and a computer system providing an interface and a grading module. The interface prompts a borrower to enter personal information, which the grading module uses to calculate the borrower’s credit grading, and allows the borrower to identify and compare loan packages in the database using the credit grading. 811 F.3d. at 1318, 117 USPQ2d at 1695. The Federal Circuit determined that these claims were directed to the concept of "anonymous loan shopping", which was a concept that could be "performed by humans without a computer." 811 F.3d. at 1324, 117 USPQ2d at 1699. Another example is Berkheimer v. HP, Inc., 881 F.3d 1360, 125 USPQ2d 1649 (Fed. Cir. 2018), in which the patentee claimed methods for parsing and evaluating data using a computer processing system. The Federal Circuit determined that these claims were directed to mental processes of parsing and comparing data, because the steps were recited at a high level of generality and merely used computers as a tool to perform the processes. 881 F.3d at 1366, 125 USPQ2d at 1652-53.
As to Applicant’s arguments on remarks page 11, that claim 1 provides one or more results of an evaluation of the package based on the assurance level and the percentage of fail rate, and therefore do not recite a judicial exception, the Examiner respectfully disagrees. This limitation as broadly recited in claim 1 recite mental process and method of organizing human activities of displaying results based on assurance level and failure rate. Therefore this is part of the abstract idea. There are no additional elements in this limitation, therefore the Examiner respectfully disagrees that the claim limitation in claim 1 amounts to a practical application and or significantly more than the judicial exception.
On Remarks pages 11-12, Applicant argues that the claims require complicated steps of obtaining data from a mountain of data which cannot be done in a reasonably relevant amount of time. While the Examiner understands Applicant’s arguments here, the Examiner respectfully disagrees. There is no requirement in the claims that the system process mountains of data as argued by Applicant. Therefore the Examiner disagrees. Further even if there was a requirement to process a certain amount of data, as broadly argued here in claim 1, which the Examiner does not contend based on the above, this broadly processing of data faster with a computer than by hand would result, as merely instructions to apply the abstract idea as defined in MPEP 2106.05(a), specifically invoking computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.
Applicant argues the 101 rejection on pages 12-13 with respect to the USPTO August 2025 guidelines. The Examiner has carefully considered Applicant’s arguments but respectfully disagrees. The Examiner believes that the Examiner has not made an overgeneralization of the claims, rather claim 1 is recited at such a high level of abstraction it reasonably recites limitations that qualify as mental process and certain method of organizing human activity steps, and the resulting additional elements of being done using “a computer based” system and collecting information from a “website” merely result in apply it or generally linking it to the field of computers which are limitations previously found by the courts to not qualify as a practical application and or significantly more (see MPEP 2106.05(h) and 2106.05(f)). Therefore the Examiner respectfully disagrees.
On Remarks pages 13-15, Applicant argues the cited prior art and Applicant’s amendments. Applicant argues differences between Applicant’s invention and the cited prior art. Specifically Applicant argues the Grant reference is more about delivery and not the package itself. While it may be true that aspects of the Grant reference are focused on the delivery, the Examiner does not see any distinction between Applicant’s claims as currently amended as argued in claim 1 and the cited combination of references.
Grant clearly teaches determining a package risk score (see paragraph 0074) based on customer reviews that include trigger words like fragile, broken, cracked etc. This reads on the amended “based on the words describing the packaging”
Therefore the Examiner respectfully disagrees.
With respect to Bandaru, Applicant argues packaging however, Bandaru was not relied upon to teach packaging in claim 1, therefore the argument in not persuasive. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
With respect to Kunetsova applicant argues negative ratings as based on the words describing the packaging, however Kunetsova is not relied upon for such argued features, rather Kunetsova is merely relied upon to teach extracting based on a low rating. Further the response to attacking the references individually See In re Keller above is applicable here as well.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., low rating related to words describing the packaging, as it is noted that the low rating in the extracting step is not tied to the negative or positive review in the categorizing step) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
With respect to Applicant’s arguments with respect to the other cited references for other claims (Stensmo, Bostick et al.) the Examiner interprets as detailed above the combination of Grant et al. (United States Patent Application Publication Number: US 2022/0101248) further in view of Bandaru et al. (United States Patent Application Publication Number: US 2008/0133488) in view of Kuznetsova et al. (United States Patent Number: US 9,405,825) to teach amended claim 1 therefore the Examiner finds the argument not persuasive as these other references have not been relied upon for the argued features.
Therefore the Examiner does not find the argument persuasive.
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 1-7 and 9-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-7 and 9-14 recite a process as the claims recite a method. Claims 15-20 recite a machine as the claims recite a system with a processor and instructions. Claim 21 recite a process as the claims recite a method.
The claim(s) 1-7 and 9-21 recite(s) determining whether a package or packaging of a product or item meets a standard or threshold based on collecting user reviews, analyzing the information according to rules, and determining the results based on the collection and analysis. The claims are recited at such a high level of generality the claims recite observations, evaluations, judgements, and opinions that can be performed in the human mind or with pen and paper, and accordingly the claims recite a mental process.
Further the claims recite managing personal behavior or relationships or interactions between people including social activities, teachings and following rules or instructions and accordingly the claims recite certain methods of organizing human activities.
Mental processes as well as certain methods of organizing human activities are in the groupings of enumerated abstracts ideas, and hence the claims recite an abstract idea.
This judicial exception is not integrated into a practical application because the claims merely recite limitations that are not indicative of integration into a practical application in that the claims merely recite:
(1) 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 (see MPEP 2106.05(f)), (2) Adding insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)). Specifically as recited in the claims, and (3) Generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). Specifically as recited in the claims:
The Examiner notes that the Examiner has underlined and bolded the additional elements for distinction. Limitations not underlined and bolded are considered part of the abstract idea.
1. A method of evaluating a package durability, the method comprising: extracting,
using a computer-based packing evaluation system, a plurality of customer reviews for a specific product from a user-identified website, wherein each customer review includes data that is used to express a customer's experience with the specific product based on a low negative rating for the specific product;
identifying, using the computer-based packaging evaluation system, one or more packaging related reviews based on a packaging list profile and the plurality of customer reviews;
categorizing, using the computer-based packaging evaluation system, whether each of the one or more packaging related reviews is a negative review or a positive review based on words describing the packaging;
determining, using the computer-based packaging evaluation system, whether the package meets an assurance level based on a percentage of failure rate associated with a plurality of negative reviews categorized, wherein the assurance level indicates whether the package provides an acceptable level of performance;
and providing one or more results of an evaluation of the package based on the assurance level and the percentage of failure rate.
2. The method of Claim 1, wherein the identifying, using the computer-based packaging evaluation system, the one or more packaging reviews comprises:
splitting, using a tokenization process of the computer-based packaging evaluation system, each customer review of the plurality of customer reviews into one or more segments of text words, wherein the one or more segments of text words includes an individual word or a phrase having two or more words;
determining, using a lemmatization process of the computer-based packaging evaluation system, a base form of each word in the one or more segments of text words;
and identifying, using the computer-based packaging evaluation system, one or more packaging related reviews based on a packaging list profile and the one or more segment of text words.
3. The method of Claim 1, wherein the categorizing, using the computer-based packaging evaluation system, whether each of the one or more packaging related reviews as a negative review or a positive review further classify the one or more package related reviews as the positive review or the negative review based a sentiment model, wherein the sentiment model is a machine learning model using natural language processing.
4. The method of Claim 1, wherein the extracting, using the computer-based packaging evaluation system, the one or more customer reviews further comprises retrieving the one or more customer reviews using a web scrapper of the computer-based packaging evaluation system.
5. The method of Claim 1, wherein the one or more customer reviews comprises one or more text words describing the customer experience with the specific product and one or more customer uploaded images associated with the one or more text words.
6. The method of Claim 1, further comprising:
identifying, using the computer-based packaging evaluation system, a number of negative reviews;
identifying, using the computer-based packaging evaluation system, a number of positive reviews;
determining, using the computer-based packaging evaluation system, a percentage of failure rate for the negative reviews based on a total number of package related reviews;
and displaying a graphical image of the percentage of failure rate for the negative reviews.
7. The method of Claim 1, further comprising:
extracting, using the computer-based packaging evaluation system using an embedded web scraper, the customer reviews from the website for the specific product based on a predetermined rating criteria;
splitting, using a tokenization process of the computer-based packaging evaluation system, each customer review into one or more text words, wherein the one or more text words includes an individual word or a phrase having two or more words;
ranking the one or more text words based on a frequency of occurrence;
and generating the packaging list profile based on one or more ranked words, wherein the packaging list profile is a library of words related to packaging.
9. The method of Claim 7, further comprising receiving, using the computer-based packaging evaluation system, one or more packaging related terms from user interface device to modify the packaging list profile.
10. The method of Claim 1, further comprising: determining that the package is acceptable, if the failure rate is lower than the assurance level;
determining that the package is unacceptable, if the failure rate is above the assurance level, the package for the product may need to be redesigned;
and providing an indicator providing whether the failure rate meets the assurance level.
11. The method of Claim 1, further comprising identifying, using the computer-based packaging evaluation system, a customer identified-problem based a relationship between two frequently occurring text words found within the plurality of negative reviews categorized, wherein the two text words includes a first text word describing a package feature and a second text word that co-occurs in association with the first text word.
12. The method of Claim 11, wherein identifying, using the computer-based packaging evaluation system, one or more customer identified-problems associated with the package comprises:
identifying a list of frequently occurring text words used within the plurality of negative reviews categorized, wherein at least one text word of the list of frequently occurring text words identifies a packaging feature;
identifying one or more relationships between a first text word of the list of frequently occurring list of text words and another frequently occurring text word of the list of text words associated with the first text word;
and determining the one or more customer identified-problems based on the one or more identified relationships.
13. The method of Claim 12, further comprising determining, using the computer-based packaging evaluation system, a number of occurrences for each identified relationship between each packaging feature of the list of frequently occurring packaging features and a frequently text word associated with a respective packaging feature.
14. The method of Claim 13, further comprising pruning, one or more identified relationships between each packaging feature of the list of frequently occurring packaging features and a frequently text word associated with a respective packaging feature to reduce the number of identified relations below a predetermined threshold.
15. A packaging evaluation system for a package, the packaging evaluation system comprising:
a processor; a non-transitory computer readable medium comprising instructions that are executable by the processor, wherein the instructions comprise:
extracting a plurality of customer reviews for a specific product from a user-identified website, wherein each customer review includes one or more text words that is used to express a customer's experience with the specific product based on a low negative rating for the specific product;
identifying one or more packaging related reviews based on a packaging list profile and the plurality of customer reviews;
categorizing, using a natural language process, whether each of the one or more packaging related reviews is a negative review or a positive review based on the words describing the packaging;
determining a customer identified-problem based a relationship between two frequently occurring text words found within the plurality of negative reviews categorized, wherein the two text words includes a first text word describing a package feature and a second text word that co-occurs in association with the first text word;
and providing one or more results of an evaluation of the package based on the customer identified problem.
16. The system of Claim 15, wherein the one or more customer reviews comprises one or more text words describing the customer experience with the specific product and one or more customer uploaded images associated with the one or more text words.
17. The system of Claim 15, wherein the instructions further comprise:
extracting, using an embedded web scrapper, the customer reviews from the website for the specific product based on a predetermined low rating criteria;
splitting, using a tokenization process, each customer review into one or more text words, wherein the one or more text words includes an individual word or a phrase having two or more words;
ranking the one or more text words based on a frequency of occurrence;
and generating the packaging list profile based on one or more ranked text words, wherein the packing list profile is a library of words related to packaging.
18. The system of Claim 15, wherein identifying one or more customer identified-problems associated with the package comprises:
identifying a list of frequently occurring text words used within a plurality of negative reviews categorized, wherein at least one text word of the list of frequently occurring text words identifies a packaging feature;
identifying one or more relationships between a first text word of the list of frequently occurring list of text words and another frequently occurring text word of the list of text words associated with the first text word;
and determining the one or more customer identified-problems based on the one or more identified relationships.
19. The system of Claim 15, wherein the instructions further comprise determining a number of occurrences for each identified relationship between each packaging feature of the list of frequently occurring packaging features and a frequently text word associated with a respective packaging feature.
20. The system of Claim 15, wherein the instructions further comprise pruning, one or more identified relationships between each packaging feature of the list of frequently occurring packaging features and a frequently text word associated with a respective packaging feature to reduce a number of identified relationships based on a predetermined threshold.
21. A method comprising:
scraping a plurality of customer reviews for a specific product from a user-identified website, wherein each customer review includes one or more text words that is used to express a customer's experience with the specific product based on a low negative rating for the specific product;
identifying one or more packaging related reviews based on a packaging list profile and the plurality of customer reviews;
categorizing, using a natural language process, whether each of the one or more packaging related reviews is a negative review or a positive review based on wording the one or more product related reviews;
identifying a customer identified-problem based a relationship between two frequently occurring text words found within the plurality of negative reviews categorized, wherein the two text words includes a first text word describing a package feature and a second text word that co-occurs in association with the first text word;
and providing one or more results of an evaluation of the package based on the customer identified problem.
As per claim 1, the claims recite limitations a human or humans could perform. Specifically the claims recite collecting data, identifying reviews, categorizing the reviews as positive or negative based on words describing the specific aspect like packaging, determining whether the reviews meet an assurance level and providing results. The additional elements that the information is from a “website” and limitations are being performed using a “computer” based system merely results in “apply it.”
Specifically the claim invokes a computer or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g. to receive, store or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea does not integrate a judicial exception into a practical application or provide significantly more.
Further limitations that could be gathered by humans or humans from example paper reviews etc. that instead recite being gathered from a website and limitations that recite method of organizing human activities and mental processes that instead recite these are performed using a “computer” based system merely generally link the use of the judicial exception to the field of computers.
As per claim 2, the claims recite limitations a human or humans could perform. Specifically the claims recite splitting up the reviews into segments words, determining a base form of each word using the text words, and identifying one of more reviews based on the words and a list profile (a list of words to compare to). The additional element that the information is performed by a “tokenization process” and a “lemmatization” process merely results in apply it. Specifically here the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it", as the claims merely recite a result-oriented solution and lack details as to how the computer performs the modifications which is equivalent to the words “apply it”. Further limitations that could be performed by humans or humans that instead recite being a performed by a “tokenization process” and a “lemmatization” process merely generally link the use of the judicial exception to the field of computers.
Further this can be considered mere data gathering in conjunction with the abstract idea.
The additional element that the mental process and human activity steps are instead being performed using a “computer” based system merely recite apply it and generally linking it to the field of computer as discussed above in claim 1.
As per claim 3, the claims recite limitations a human or humans could perform. Specifically the claims recite categorizing a review as positive or negative based on a sentiment model. The additional element that the information is performed by a “machine learning model using natural language processing” merely results in apply it. Specifically here the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it", as the claims merely recite a result-oriented solution and lack details as to how the computer performs the modifications which is equivalent to the words “apply it”. Here there are no details about a particular machine learning model or how it operates to derive information other than it being used to determine sentiment of being positive or negative. The machine learning model is used to generally apply the abstract idea without placing any limitation on how it operates to derive the information. (Examiner’s note: for reference with respect to artificial intelligence see USPTO July 2024 Subject Matter eligibility examples, Example 48). Further limitations that could be performed by humans or humans from looking and analyzing reviews that instead recite being a performed by a “machine learning model using natural language processing” merely generally link the use of the judicial exception to the field of computers.
The additional element that the mental process and human activity steps are instead being performed using a “computer” based system merely recite apply it and generally linking it to the field of computer as discussed above in claim 1.
As per claim 4, the claims recite limitations a human or humans could perform. Specifically a user could gather reviews to analyze. The additional element that the information is received from a web scrapper merely results in apply it as the claims merely recite a result-oriented solution and lack details as to how the computer performs the modifications which is equivalent to the words “apply it”. Further limitations that could be performed by humans or humans from looking and analyzing reviews that instead recite being a performed by a “a web scrapper” merely generally link the use of the judicial exception to the field of computers.
Further this can be considered mere data gathering in conjunction with the abstract idea.
The additional element that the mental process and human activity steps are instead being performed using a “computer” based system merely recite apply it and generally linking it to the field of computer as discussed above in claim 1.
As per claim 5, the claims recite limitations a human or humans could perform. The claims recite customer reviews comprise images and text associated. The additional element that this information is “uploaded” merely results in “apply it.” Specifically the claim invokes a computer or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g. to receive, store or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea does not integrate a judicial exception into a practical application or provide significantly more. Further limitations that could be gathered by humans or humans from example paper reviews etc. that instead recite the information being “uploaded” merely generally link the use of the judicial exception to the field of computers.
Further this can be considered mere data gathering in conjunction with the abstract idea.
As per claim 6, the claims recite limitations a human or humans could perform. Specifically the claims recite identifying a number of positive and negative reviews, determining a percentage fail rate based on the number of reviews and displaying a graphical image based on that calculation.
The additional element that the mental process and human activity steps are instead being performed using a “computer” based system merely recite apply it and generally linking it to the field of computer as discussed above in claim 1.
As per claim 7, the claims recite limitations a human or humans could perform. Specifically the claims recite collecting customer reviews from a website based on a specific rating criteria , splitting each customer review into text words, ranking based on frequency, and generating a packing list based on those ranked words which is related to packaging. The additional elements that the splitting is performed by a “tokenization” process, the collecting is performed by a web scraper, and the information is from a website results in the same analysis as found above in claims 1-2 and 4 above.
The additional element that the mental process and human activity steps are instead being performed using a “computer” based system merely recite apply it and generally linking it to the field of computer as discussed above in claim 1.
As per claim 9, the claims recite limitations a human or humans could perform. Specifically the claims recite receiving one or more packaging related terms from a user to modify a packing list profile. The additional element that this is received from a “device interface device” rather than just from a user merely results in “apply it.” Specifically the claim invokes a computer or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g. to receive, store or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea does not integrate a judicial exception into a practical application or provide significantly more. Further limitations that could be gathered by humans or humans that instead recite from a “user interface device” merely generally link the use of the judicial exception to the field of computers.
The additional element that the mental process and human activity steps are instead being performed using a “computer” based system merely recite apply it and generally linking it to the field of computer as discussed above in claim 1.
As per claim 10, the claims recite limitations a human or humans could perform. Specifically the claims recite determining if the package is acceptable or unacceptable based on assurance level and providing an indicator based on the assurance level. There are no additional elements beyond those previously recited in claim.
As per claim 11, the claims recite limitations a human or humans could perform. Specifically the claims recite determining a customer identified problem based on a relationship between two frequently occurring text words found in negative reviews.
The additional element that the mental process and human activity steps are instead being performed using a “computer” based system merely recite apply it and generally linking it to the field of computer as discussed above in claim 1.
As per claim 12, the claims recite limitations a human or humans could perform. Specifically the claims recite identifying one or more customer problem associated with the package by identifying a list of frequently occurring text words, identifying one or more relationships between a first word of the list of frequently occurring list of text words and determining one or more customer identified problems based on the identified relationship.
The additional element that the mental process and human activity steps are instead being performed using a “computer” based system merely recite apply it and generally linking it to the field of computer as discussed above in claim 1.
As per claim 13, the claims recite limitations a human or humans could perform. Specifically the claims recite determining a number of occurrences for each identified relationship between each packaging feature of the list of frequency occurring packaging features and a frequently text word associated with a respective packaging feature.
The additional element that the mental process and human activity steps are instead being performed using a “computer” based system merely recite apply it and generally linking it to the field of computer as discussed above in claim 1.
As per claim 14, the claims recite limitations a human or humans could perform. Specifically the claims recite reducing the number of identified relationships below a predetermined threshold. The additional element that the information is performed by a “pruning” process merely results in apply it. Specifically here the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it", as the claims merely recite a result-oriented solution and lack details as to how the computer performs the modifications which is equivalent to the words “apply it”. Further limitations that could be gathered by humans or humans that instead recite being a performed by a “pruning” process merely generally link the use of the judicial exception to the field of computers.
As per claim 15, the claims recite limitations a human or humans could perform. Specifically the claims recite collecting data, identifying reviews, categories the reviews as positive or negative based on words describing the specific aspect like packaging, determining whether the reviews meet an assurance level and providing results. The additional element that the information is from a “website” and performed by “a processor; a non-transitory computer readable medium comprising instructions that are executable by the processor, wherein the instructions comprise” merely results in “apply it.”
Specifically the claim invokes a computer or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g. to receive, store or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea does not integrate a judicial exception into a practical application or provide significantly more. Further limitations that could be gathered by humans or humans from example paper reviews etc. and performed by humans that instead recite being gathered instead from a website and performed by “ a processor; a non-transitory computer readable medium comprising instructions that are executable by the processor, wherein the instructions comprise” merely generally link the use of the judicial exception to the field of computers.
The additional element that determining whether the review is positive or negative is performed by a “natural language process” merely results in apply it. Specifically here the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it", as the claims merely recite a result-oriented solution and lack details as to how the computer performs the modifications which is equivalent to the words “apply it”. Further limitations that could be performed by humans or humans that instead recite being performed by a “natural language process” merely generally link the use of the judicial exception to the field of computers.
As per claim 16, the claims recite limitations a human or humans could perform. The claims recite customer reviews comprise images and text associated. The additional element that this information is “uploaded” merely results in “apply it.” Specifically the claim invokes a computer or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g. to receive, store or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea does not integrate a judicial exception into a practical application or provide significantly more. Further limitations that could be gathered by humans or humans from example paper reviews etc. that instead recite the information being “uploaded” merely generally link the use of the judicial exception to the field of computers.
Further this can be considered mere data gathering in conjunction with the abstract idea.
As per claim 17, the claims recite limitations a human or humans could perform. Specifically the claims recite collecting customer reviews from a website based on a specific rating criteria , splitting each customer review into text words, ranking based on frequency, and generating a packing list based on those ranked words which is related to packaging. The additional elements that the splitting is performed by a “tokenization” process, the collecting is performed by a web scraper, and the information is from a website results in the same analysis as found above in claims 1-2, 4, and 15 above
As per claim 18, the claims recite limitations a human or humans could perform. Specifically the claims recite identifying one or more customer problem associated with the package by identifying a list of frequently occurring text words, identifying one or more relationships between a first word of the list of frequently occurring list of text words and determining one or more customer identified problems based on the identified relationship. There are no additional elements beyond those previously recited in claim.
As per claim 19, the claims recite limitations a human or humans could perform. Specifically the claims recite determining a number of occurrences for each identified relationship between each packaging feature in the list of frequently occurring packaging features and a frequently text word associated with a respective packaging feature. There are no additional elements beyond those previously recited in claim.
As per claim 20, the claims recite limitations a human or humans could perform. Specifically the claims recite reducing the number of identified relationships below a predetermined threshold. The additional element that the information is performed by a “pruning” process merely results in apply it. Specifically here the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it", as the claims merely recite a result-oriented solution and lack details as to how the computer performs the modifications which is equivalent to the words “apply it”. Further limitations that could be performed by humans or humans f that instead recite being a performed by a “pruning” process merely generally link the use of the judicial exception to the field of computers.
As per claim 21, the claims recite limitations a human or humans could perform. Specifically the claims recite collecting data, identifying reviews, categorizing the reviews as positive or negative based on words describing the specific aspect like packaging, identifying a problem based on text and feature rules, providing results. The additional element that the information is from a “website” and performed by merely results in “apply it.” Specifically the claim invokes a computer or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g. to receive, store or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea does not integrate a judicial exception into a practical application or provide significantly more. Further limitations that could be gathered by humans or humans from example paper reviews etc. and performed by humans that instead recite being gathered instead from a website merely generally link the use of the judicial exception to the field of computers.
The additional element that determining whether the review is positive or negative is performed by a “natural language process” merely results in apply it. Specifically here the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it", as the claims merely recite a result-oriented solution and lack details as to how the computer performs the modifications which is equivalent to the words “apply it”. Further limitations that could performed by humans or humans that instead recite being a performed by a “natural language process” merely generally link the use of the judicial exception to the field of computers.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims merely recite limitations that are not indicative of an inventive concept (“significantly more”) in that the claims merely recite: (1) 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 (see MPEP 2106.05(f)), (2) Adding insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)), and (3) Generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)), as detailed above.
Further with respect to the above data gathering the limitations merely recite:
(1) Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (see MPEP 2106.05(d) and Berkheimer Memo). Specifically as recited in the claims:
(a) by tokenization (see claims 2, 7, and 17)
- Kephart et al. (United States Patent Application Publication Number: US 2001/0042087) paragraph 0012 “Conventionally, text classifiers learn to predict the category of a document by training on a corpus of previously labeled documents. Text classifiers make their predictions by comparing the frequency of tokens within a document to the average frequency of tokens in documents appearing in each category. A token is any semantically meaningful sequence of characters appearing in the document, such as a word, multi-word phrase, number, date or abbreviation. For example, the text "The Civil War ended in 1865" might be tokenized into the token set {"The", "Civil War", "ended", "in", "1865" }. Note that "Civil War" is interpreted here as a single token. The art of tokenization, as described in Salton et al., Introduction to Modern Information Retrieval, McGraw-Hill Book Company, 1983, is well known to those in the skilled in the art.”
- Chidiac et al. (United States Patent Application Publication Number: US 2005/0125311) paragraph 0029 “Tokenization is the step of breaking down the textual information about the part into a set of strings according to some rules, such strings being words, phrases, or character strings. Those skilled in the art will appreciate that several different methods for tokenization are available in the open literature. One common method of doing tokenization simply involves the breaking down of text into words based on whitespace and punctuation. Another widely used method involves the use of n-grams (substrings of length "n") where the text is broken down into tokens consisting of contiguous sequences of "n" characters, where "n" is an integer such as 1, 2, and so on. See, for example, R. Baeza-Yates and B. Ribeiro-Neto, Modern Information Retrieval, Addison Wesley Press (1999). One or more such tokenization techniques may be applied, either independently or in sequence, to generate a set of tokens. Similarly, those skilled in the art will appreciate that several different methods for converting these tokens to features have been described and used in the open literature. One common method involves the creation of a binary feature for each token seen in the data set. The ttextual information about the part in a BOM item is then replaced by a list of features created of all the tokens seen in the data set, a feature taking a value of one if the associated token exists in the part information in that BOM item, and a value zero otherwise. Feature selectors 308 consist of methods for determining a subset of features created by the feature-extractors that are the most appropriate and useful for creating the most accurate models. Those skilled in the art will appreciate that several different methods of feature selection are available in the open literature. One common method, called document frequency, is based on the number of times a particular feature is observed in the part information for the various BOM items. See, for example, Y. Yang and J. O. Pedersen, "A Comparative Study on Feature Selection in Text Categorization", Proc. of the 14.sup.th International Conference on Machine Learning ICML97, pp. 412-420, 1997. The idea behind this approach is that more frequent features are more useful for classification than less frequent features. Another approach based on the same principle, called the CHI test, uses a statistical test to determine which features are more relevant.”
- Whitman et al. (United States Patent Application Publication Number: US 2013/0262089) paragraph 0047 “As one of ordinary skill in the art will appreciate, tokenization is a process of breaking up a series of words, phrases, or symbols into individual elements. This is accomplished by analyzing the series of words for certain elements such as spaces, punctuation, and separates possessives which indicate boundaries between words. For example, in FIG. 2A the artist name "Dave Matthews Band" in document 201 has been tokenized into three document tokens: "Dave", "Matthews", and "Band". In an inverted full text index, the position of each document token within the document is retained. Thus, as shown in FIGS. 2A, 2B, 2C, and 2D, each document token includes a position value corresponding to its position within the artist name.”
(b) by lemmatization (see claim 2)
- Moreau et al. (United States Patent Application Publication Number: US 2017/0017971) paragraph 0049 “ As would be understood by one skilled in the relevant art, lemmatization is an algorithmic process of determining the lemma for a given word. Since the lemmatization process may involve complex tasks such as, for example, understanding context and determining the part of speech of a word in a sentence (which can require, for example, knowledge of the grammar of a language), it can be complex to implement a lemmatizer for a new language. Then, an embodiment can perform POS tagging, which is a process of classifying words into their parts of speech and labeling them accordingly. After POS tagging, it is known whether a word is a noun, proper noun, verb, adjective, pronoun, article, etc. For the purpose of text mining, nouns and proper nouns occurring in a page are identified. Examples of such nouns can include, for example, camera, battery, design, display, and screen. These nouns and proper nouns allow the process to identify all the subjects which particular text discusses or describes. The text mining process determines nouns and proper nouns in order of their frequency in the normalized text. For example, the following NLTK script can be used for the text mining process:”
- Gaither et al. (United States Patent Application Publication Number: US 2017/0046622) paragraph 0020 “In additional embodiments, such normalization may include stemming and/or lemmatization, which are known in the art. In such embodiments, the stemming and/or lemmatization could be utilized to, for example, remove suffixes, or the like, from words occurring within labels. This may ensure words within labels that differ only in, for example, plural ending or verb tense would map to the same token, or label.”
-Magliozzi et al. (United States Patent Application Publication Number: US 2018/0131645) paragraph 0132 “ As understood by those of skill in the art of linguistics, lemmatization usually refers to doing things properly with the use of a vocabulary and morphological analysis of words, normally aiming to remove inflectional endings only and to return the base or dictionary form of a word, which is known as the lemma. The lemma in linguistic morphology is used to cite the lexeme of a word (the root meaning of many forms/inflections of a word).) At 612a and 612b, the neural network 600 reduces the dimension of the components with linear dense layer. Reducing the dimension improves the efficiency of the neural network 600.”
(c) web scrapper (see claims 4, 7, and 17)
- Balassanian et al. (United States Patent Application Publication Number: US 2013/0254181) paragraph 0004 “ Web scrapers and their use are well known in the art. Briefly, web scrapers collect information from web pages. Scrapers operate by extracting data from within one or more individual web pages. Commonly, regardless of now the data is extracted, it is normalized for storage in a unified format.”
-Newman (United States Patent Application Publication Number: US 2020/0074300) paragraph 0115 “In these embodiments, the classification server computer 102 comprises a web scrapper or web crawler 202 for “crawling” through a plurality of external servers 106 such as a plurality of external web servers to collect tender information published thereon. As those skilled in the art will appreciate, the web scraper 202 may be implemented in any suitable technology. For example, in some embodiments, the web scrapper 202 is implemented using Scrapy, an open source web-crawling framework offered by Scrapinghub, Ltd. of Cork, Ireland.”
- Gregorie et al. (United States Patent Application Publication Number: US 2006/0026114) paragraph 0028 “Those of ordinary skill in the art will be familiar with the variety of crawlers and similar software mechanisms available for locating websites and extracting bits of information from the located websites. As the operations and the programming involved in locating websites and extracting information from websites is well understood in the art, the present application does not provide additional detail regarding this aspect of the system 10.”
(d) receiving or transmitting data over a network, e.g. using the Internet to gather data (see claim 5 and 16) (see MPEP 2106.05(d) Well-Understood, Routine, Conventional Activity [R-07.2022][cited herein): Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added))
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 1-7 and 15-21 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.
As per claim 1, Applicant recites as amended based on the words describing the packaging. There is insufficient antecedent basis for the limitation, the words describing the packaging, as the limitation has not previously been recited in the claim. For the purposes of this examination, the Examiner will interpret the claim as follows: based on
Further claims 2-7 and 9-14 are rejected based on their dependency on claim 1 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.
As per claim 15, Applicant recites as amended based on the words describing the packaging. There is insufficient antecedent basis for the limitation, the words describing the packaging, as the limitation has not previously been recited in the claim. For the purposes of this examination, the Examiner will interpret the claim as follows: based on
Further claims 16-20 are rejected based on their dependency on claim 15 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.
As per claim 21, Applicant recites as amended based on wording of the one or more product related views. There is insufficient antecedent basis for the limitation, the one or more product related views, as the limitation has not previously been recited in the claim. For the purposes of this examination, the Examiner will interpret the claim as follows: based on wording of
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 (i.e., changing from AIA to pre-AIA ) 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.
Claim(s) 1, 3-7, 9-13, 15-19, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Grant et al. (United States Patent Application Publication Number: US 2022/0101248) further in view of Bandaru et al. (United States Patent Application Publication Number: US 2008/0133488) in view of Kuznetsova et al. (United States Patent Number: US 9,405,825).
As per claim 1, Grant et al. teaches A method of evaluating a package durability, the method comprising: (see paragraph 0007, Examiner’s note: method for identifying and communicating package delivery risks to reduce package damage during delivery).
extracting, using a computer-based packaging evaluation system, (see paragraphs 0099-0101, Examiner’s note: software running on a computer to perform the method).
a plurality of customer reviews for a specific product from a user-identified source, wherein each customer review includes data that is used to express a customer's experience with the specific product based on extraction constraints for the specific product; identifying, using the computer-based packaging evaluation system, one or more packaging related reviews based on a packaging list profile and the plurality of customer reviews; categorizing, using the package evaluation system, whether each of the one or more packaging related reviews meets certain constraints or conditions based on the words describing the packaging; determining, using the computer-based packaging evaluation system, whether the package meets an assurance level based on a percentage of failure rate associated with a plurality of reviews, wherein the assurance level indicates whether the package provides an acceptable level of performance; (see paragraphs 0041-0042, 0066, 0074-0075, Examiner’s note: teaches determining a risk score based on natural language processing of reviews like how often does an item get returned due to a reason of being a damaged package).
and providing one or more results of an evaluation of the package based on the assurance level and the percentage of failure rate. (see paragraph 0047-0048, Examiner’s note: best delivery practice, risk calculation, and communicating that results of evaluation
Grant does not expressly teach (1) receiving review information from a website, (2) categorizing reviews as positive or negative or more specifically as recited in the claims categorizing, using the evaluation system, whether each of the one or more related reviews is a negative review or a positive review and negative reviews categorized, and (3) extracting information based on a low negative rating.
However, Bandaru et al. which is in the art of analyzing user generated content (see abstract and title) teaches (1) receiving review information from a website (see paragraphs 0013 and 0035, Examiner’s note: teaches crawling information from a website).
and (2) categorizing reviews as positive or negative or more specifically as recited in the claims categorizing, using the evaluation system, whether each of the one or more related reviews is a negative review or a positive review and negative reviews categorized (see paragraph 0077, 0172-0173, and Figure 16A, Examiner’s note: each review itself can be rated as positive, negative, or neutral sentiment using the same approach).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Grant with the aforementioned teachings from Bandaru et al. with the motivation of providing a known way to collect data from different websites (see paragraphs Bandaru et al.0013 and 0035) and another way to summarize the reviews (see Bandaru et al. paragraph 0077), when collecting information from different sources online (see Grant paragraphs 0041-0042, 0066, and 0074-0075) and summarizing the review information is known (see Grant paragraphs 0041-0042, 0066, and 0074-0075).
Grant in view of Bandaru et al. does not expressly teach (3) extracting information based on a low negative rating.
However, Kuznetsova et al. which is in the art of automatic online review interpretation (see abstract) teaches (3) extracting information based on a low negative rating (see column 7 lines 23-42, Examiner’s note: extracting information for combined reviews based on being above or below a threshold).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Grant in view of Bandaru et al. with the aforementioned teachings from Kuznetsova et al. with the motivation of providing another known constraint to filter user reviews or ratings information to perform analysis on (see Kuznetsova et al. column 7 lines 23-42), when filtering user review information based on numerous different types of constraints (see Grant paragraphs 0041-0042 and Bandaru et al. paragraphs 0097 and 0128) is known in both the references of Grant and Bandaru et al.
As per claim 3, Grant teaches
Using the computer-based packaging evaluation system, to determine the one or more packaging related reviews (see paragraphs 0041-0042, 0066, 0074-0075, Examiner’s note: teaches determining a risk score based on natural language processing of reviews like how often does an item get returned due to a reason of being a damaged package).
Grant does not expressly teach categorizing positive and negative reviews according to a sentiment model or more specifically as recited in the claims wherein the categorizing whether each of the one or more reviews as a negative review or a positive review further classify the one or more reviews as the positive review or the negative review based a sentiment model, wherein the sentiment model is a machine learning model using natural language processing.
However, Bandaru et al. which is in the art of analyzing user generated content (see abstract and title) teaches categorizing positive and negative reviews according to a sentiment model or more specifically as recited in the claims wherein the categorizing whether each of the one or more reviews as a negative review or a positive review further classify the one or more reviews as the positive review or the negative review based a sentiment model, wherein the sentiment model is a machine learning model using natural language processing (see paragraph 0034, 0041, 0055, 0077, and 0141, Examiner’s note: a computer model that provides up to date information where each review itself can be rated as positive, negative, or neutral sentiment using the same approach (see paragraphs 0034, 0077, 0141). Further the system uses natural language processing (see paragraph 0041 and 0055)).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Grant in view of Bandaru et al. in view of Kuznetsova et al. with the aforementioned teachings from Bandaru et al. with the motivation of providing a known way to summarize reviews (see Bandaru et al. paragraph 0077), when summarizing the information is known (see Grant paragraphs 0041-0042, 0066, 0074-0075).
As per claim 4, Grant teaches
wherein the extracting, using the computer-based packaging evaluation system, the one or more customer reviews further comprises retrieving the one or more customer reviews using techniques of the computer-based packaging evaluation system (see paragraphs 0042-0043, Examiner’s note: teaches receiving information from a specific source).
Grant does not expressly teach a web scrapper.
However, Bandaru et al. which is in the art of analyzing user generated content (see abstract and title) teaches (1) a web scrapper (see paragraphs 0013 and 0035, Examiner’s note: teaches crawling information from a website).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Grant in view of Bandaru et al. in view of Kuznetsova et al. with the aforementioned teachings from Bandaru et al. with the motivation of providing a known way to collect data from different websites (see paragraphs Bandaru et al.0013 and 0035), when collecting information from different sources online (see Grant paragraphs 0041-0042, 0066, 0074-0075) is known.
As per claim 5, Grant teaches
wherein the one or more customer reviews comprises one or more text words describing the customer experience with the specific product (see paragraphs 0041-0042, 0066, 0074-0075, Examiner’s note: teaches determining a risk score based on natural language processing of reviews like how often does an item get returned due to a reason of being a damaged package).
Grant does not expressly teach the reviews include images or more specifically as recited in the claims of and one or more customer uploaded images associated with the one or more text words.
However, Bandaru et al. which is in the art of analyzing user generated content (see abstract and title) teaches the reviews include images or more specifically as recited in the claims of and one or more customer uploaded images associated with the one or more text words (see paragraph 0039, Examiner’s note: images in reviews).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Grant in view of Bandaru et al. in view of Kuznetsova et al. with the aforementioned teachings from Bandaru et al. with the motivation of providing a commonly known element in online reviews that they can include images (see Bandaru et al. paragraph 0039), when Grant teaches online user reviews (see Grant paragraphs 0041-0042, 0066, and 0074-0075) are known.
As per claim 6, Grant teaches
further comprising: identifying, using the computer-based packaging evaluation system, a number of negative reviews; identifying, using the computer-based packaging evaluation system, additional information; determining, using the computer-based packaging evaluation system, a percentage of failure rate for the negative reviews based on a total number of package related reviews; and displaying a graphical image of the percentage of failure rate for the negative reviews (see paragraphs 0074 and 0048, Examiner’s note: determining a package risk score from information like how often does an item get returned due toa reason of being a damaged package and providing the risk score on the user device).
Grant does not expressly teach identifying, a number of positive reviews.
However, Bandaru et al. which is in the art of analyzing user generated content (see abstract and title) teaches identifying, a number of positive reviews (see paragraphs 0077 and 0172-0173, Examiner’s note: teaches determining a number of positive reviews).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Grant in view of Bandaru et al. in view of Kuznetsova et al. with the aforementioned teachings from Bandaru et al. with the motivation of determining a common feature of online reviews of how many are positive or negative to provide that information to an interested party (see Bandaru et al. paragraphs 0077 and 0172-0173), when determining information about negative or positive aspects of the review to provide that information an interest party (see Grant paragraphs 0074 and 0048) is known.
As per claim 7, Grant teaches
further comprising: extracting, using the computer-based packaging evaluation system using an a technique, the customer reviews from the source for the specific product; (see paragraphs 0042-0043, Examiner’s note: receiving reviews according to a specific threshold or range for example).
splitting, using a process of the computer-based packaging evaluation system, each customer review into one or more text words, wherein the one or more text words includes an individual word or a phrase having two or more words; wherein the packaging list profile is a library of words related to packaging. (see paragraphs 0041-0042, 0066, 0074-0075, Examiner’s note: teaches determining a risk score based on natural language processing of reviews like how often does an item get returned due to a reason of being a damaged package).
Grant does not teach (1) embedded web scraper, (2) the source being a website, (3) a tokenization process, (4) ranking the one or more text words based on a frequency of occurrence; and generating the review list profile based on one or more ranked words, wherein the review list profile is a library of words related to review.
However, Bandaru et al. which is in the art of analyzing user generated content (see abstract and title) teaches
(1) embedded web scraper, (see paragraphs 0013 and 0035, Examiner’s note: teaches crawling information from a website).
(2) the source being a website, (see paragraphs 0013 and 0035, Examiner’s note: teaches crawling information from a website).
(3) a tokenization process, (see paragraphs 0014, 0040, 0054-0055, Examiner’s note: sentence segmentation through tokenization process).
(4) ranking the one or more text words based on a frequency of occurrence; and generating the review list profile based on one or more ranked words, wherein the review list profile is a library of words related to review (see paragraph 0099, Examiner’s note: teaches based on the frequency and contextual relevance of such exception tokens they may bubble up to the point where they can be fed automatically into the domain noun collection).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Grant in view of Bandaru et al. in view of Kuznetsova et al. with the aforementioned teachings from Bandaru et al. with the motivation of providing a known way to extract words from the reviews (see Bandaru et al. paragraphs 0014, 0040, 0054-0055) and update the information over time as the machine learnings from collected data (see Bandaru et al. paragraph 0099), when extracting words from reviews (see Grant paragraphs 0041-0042, 0066, 0074-0075) and updating information over time is known (see Grant paragraph 0070).
As per claim 9, Grant teaches
further comprising receiving, using the computer-based packaging evaluation system, one or more packaging related terms from user interface device to modify the packaging list profile (see paragraph 0070 and 0078, Examiner’s note: updating constraints over time (see paragraph 0070) where these updates are from information received which can come from mobile devices (see paragraphs 0078-0079)).
As per claim 10, Grant teaches
further comprising: determining that the package is acceptable, if the failure rate is lower than the assurance level; determining that the package is unacceptable, if the failure rate is above the assurance level, the package for the product may need to be redesigned; and providing an indicator providing whether the failure rate meets the assurance level (see paragraphs 0044-0047,0049, and 0069 Examiner’s note: high and low scores, and communicating that information with the delivery person so they know how to handle the package like to not ross or do not stack the package if the risk score is high).
As per claim 11, Grant teaches
further comprising identifying, using the computer-based packaging evaluation system, a customer identified-problem describing a package feature (see paragraphs 0041-0042, 0066, 0074-0075, Examiner’s note: teaches determining a risk score based on natural language processing of reviews like how often does an item get returned due to a reason of being a damaged package)
Grant does not expressly disclose identifying a sentiment or problem based a relationship between two frequently occurring text words found within the plurality of negative reviews categorized, wherein the two text words includes a first text word describing a feature and a second text word that co-occurs in association with the first text word.
However, Bandaru et al. which is in the art of analyzing user generated content (see abstract and title) teaches identifying a sentiment or problem based a relationship between two frequently occurring text words found within the plurality of negative reviews categorized, wherein the two text words includes a first text word describing a feature and a second text word that co-occurs in association with the first text word (see paragraphs 0057, 0062-0066, 0077-0078, 0099, 0101, 0117-0126, Examiner’s note: teaches here how the combination of multiple words can be used to determine sentiment where sentiment may be positive or negative and can also be based on frequency occurring or reoccurring words).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Grant in view of Bandaru et al. in view of Kuznetsova et al. with the aforementioned teachings from Bandaru et al. with the motivation of providing a known way to extract information from different sources based on context (see Bandaru et al. paragraphs 0057, 0062-0066, 0077-0078, 0117-0126), when extracting information from different sources based on content is known (see Grant paragraphs 0041-0042, 0066, 0074-0075).
As per claim 12, Grant teaches
wherein identifying, using the computer-based packaging evaluation system, one or more customer identified-problems associated with the package comprises: text words identifies a packaging feature and determining the one or more customer identified-problems based on the one or more identified relationships. (see paragraphs 0041-0042, 0066, 0074-0075, Examiner’s note: teaches determining a risk score based on natural language processing of reviews like how often does an item get returned due to a reason of being a damaged package)
Grant does not expressly disclose identifying a list of frequently occurring text words used within the plurality of negative reviews categorized, wherein at least one text word of the list of frequently occurring text words identifies a feature; identifying one or more relationships between a first text word of the list of frequently occurring list of text words and another frequently occurring text word of the list of text words associated with the first text word.
However, Bandaru et al. which is in the art of analyzing user generated content (see abstract and title) teaches identifying a list of frequently occurring text words used within the plurality of negative reviews categorized, wherein at least one text word of the list of frequently occurring text words identifies a feature; identifying one or more relationships between a first text word of the list of frequently occurring list of text words and another frequently occurring text word of the list of text words associated with the first text word (see paragraphs 0057, 0062-0066, 0077-0078, 0099, 0101, 0117-0126, Examiner’s note: teaches here how the combination of multiple words can be used to determine sentiment where sentiment may be positive or negative and can also be based on frequency occurring or reoccurring words).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Grant in view of Bandaru et al. in view of Kuznetsova et al. with the aforementioned teachings from Bandaru et al. with the motivation of providing a known way to extract information from different sources based on context (see Bandaru et al. paragraphs 0057, 0062-0066, 0077-0078, 0117-0126), when extracting information from different sources based on content is known (see Grant paragraphs 0041-0042, 0066, 0074-0075).
As per claim 13, Grant teaches
further comprising determining, using the computer-based packaging evaluation system, packaging features (see paragraphs 0041-0042, 0066, 0074-0075, Examiner’s note: teaches determining a risk score based on natural language processing of reviews like how often does an item get returned due to a reason of being a damaged package)
Grant does not expressly teach calculating the frequency of words used in the reviews or more specifically as recited in the claims a number of occurrences for each identified relationship between each review feature of the list of frequently occurring review features and a frequently text word associated with a respective review feature
However, Bandaru et al. which is in the art of analyzing user generated content (see abstract and title) teaches calculating the frequency of words used in the reviews or more specifically as recited in the claims a number of occurrences for each identified relationship between each review feature of the list of frequently occurring review features and a frequently text word associated with a respective review feature (see paragraphs 0099-0102, Examiner’s note: updating over time based on the frequency of words).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Grant in view of Bandaru et al. in view of Kuznetsova et al. with the aforementioned teachings from Bandaru et al. with the motivation of providing a known way to extract and interpret information over time as based on information learned over time (see Bandaru et al. paragraphs 0099-0102), when extracting information from different sources based on content (see Grant paragraphs 0041-0042, 0066, 0074-0075) and updating that information over time (see Grant paragraph 0070) are both known.
As per claim 15, Grant teaches A packaging evaluation system for a package, the packaging evaluation system comprising: (see paragraph 0016, Examiner’s note: system uses a computer for identifying and communicating package delivery risks to reduce package damage risk during delivery).
a processor; a non-transitory computer readable medium comprising instructions that are executable by the processor, wherein the instructions comprise: (see paragraphs 0053 and 0056, Examiner’s note: software running on a computer to perform operations).
extracting a plurality of customer reviews for a specific product from a user-identified source, wherein each customer review includes one or more text words that is used to express a customer's experience with the specific product based on extraction constraints for the specific product; identifying one or more packaging related reviews based on a packaging list profile and the plurality of customer reviews; determining information/features about the packaging based on the words describing the packaging; and package feature (see paragraphs 0041-0042, 0066, 0074-0075, Examiner’s note: teaches determining a risk score based on natural language processing of reviews like how often does an item get returned due to a reason of being a damaged package).
and providing one or more results of an evaluation of the package based on the customer identified problem. (see paragraph 0047-0048, Examiner’s note: best delivery practice, risk calculation, and communicating that results of evaluation
Grant does not expressly teach (1) the source is a website, (2) categorizing, using a natural language process, whether each of the one or more review related reviews is a negative review or a positive review; and (3) determining a customer identified-problem based a relationship between two frequently occurring text words found within the plurality of negative reviews categorized, wherein the two text words includes a first text word describing a review feature and a second text word that co-occurs in association with the first text word, and (4) extracting information based on a low negative rating.
However, Bandaru et al. which is in the art of analyzing user generated content (see abstract and title) teaches
(1) the source is a website (see paragraphs 0013 and 0035, Examiner’s note: teaches crawling information from a website).
(2) categorizing, using a natural language process, whether each of the one or more review related reviews is a negative review or a positive review (see paragraphs 0077, 0172-0173, and Figure 16A, Examiner’s note: each review itself can be rated as positive, negative, or neutral sentiment using the same approach).
(3) determining a customer identified-problem based a relationship between two frequently occurring text words found within the plurality of negative reviews categorized, wherein the two text words includes a first text word describing a review feature and a second text word that co-occurs in association with the first text word(see paragraphs 0057, 0062-0066, 0077-0078, 0099, 0101, 0117-0126, Examiner’s note: teaches here how the combination of multiple words can be used to determine sentiment where sentiment may be positive or negative and can also be based on frequency occurring or reoccurring words).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Grant in view of Bandaru et al. with the aforementioned teachings from Bandaru et al. with the motivation of providing a way to collect data from different websites (see paragraphs Bandaru et al.0013 and 0035) and another way to summarize the reviews (see Bandaru et al. paragraph 0077), when extracting information from different sources based on content and summarizing content are both known (see Grant paragraphs 0041-0042, 0066, 0074-0075).
Grant in view of Bandaru et al. does not expressly teach (4) extracting information based on a low negative rating.
However, Kuznetsova et al. which is in the art of automatic online review interpretation (see abstract) teaches (4) extracting information based on a low negative rating (see column 7 lines 23-42, Examiner’s note: extracting information for combined reviews based on being above or below a threshold).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Grant in view of Bandaru et al. with the aforementioned teachings from Kuznetsova et al. with the motivation of providing another known constraint to filter information user reviews or ratings information to perform analysis on (see Kuznetsova et al. column 7 lines 23-42), when filtering user review information based on numerous different types of constraints (see Grant paragraphs 0041-0042 and Bandaru et al. paragraphs 0097 and 0128) is known in both the references of Grant and Bandaru et al.
As per claim 16, Grant teaches
wherein the one or more customer reviews comprises one or more text words describing the customer experience with the specific product (see paragraphs 0041-0042, 0066, 0074-0075, Examiner’s note: teaches determining a risk score based on natural language processing of reviews like how often does an item get returned due to a reason of being a damaged package).
Grant does not expressly teach the reviews include images or more specifically as recited in the claims of and one or more customer uploaded images associated with the one or more text words.
However, Bandaru et al. which is in the art of analyzing user generated content (see abstract and title) teaches the reviews include images or more specifically as recited in the claims of and one or more customer uploaded images associated with the one or more text words (see paragraph 0039, Examiner’s note: images in reviews).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Grant in view of Bandaru et al. in view of Kuznetsova et al. with the aforementioned teachings from Bandaru et al. with the motivation of providing a commonly known element in online reviews that they can include images (see Bandaru et al. paragraph 0039), when Grant teaches online user reviews (see Grant paragraphs 0041-0042, 0066, and 0074-0075) are known.
As per claim 17, Grant teaches
extracting, using a technique, the customer reviews from the source for the specific product based on a predetermined rating criteria; (see paragraphs 0042-0043, Examiner’s note: receiving reviews according to a specific threshold or range for example).
splitting, using a process of the packing evaluation system, each customer review into one or more text words, wherein the one or more text words includes an individual word or a phrase having two or more words; the packaging list profile and wherein the packing list profile is a library of words related to packaging. (see paragraphs 0041-0042, 0066, 0074-0075, Examiner’s note: teaches determining a risk score based on natural language processing of reviews like how often does an item get returned due to a reason of being a damaged package).
Grant does not teach (1) embedded web scraper, (2) the source being a website, (3) a tokenization process, (4) ranking the one or more text words based on a frequency of occurrence; and generating the review list profile based on one or more ranked words, and (5) extracting based on a predetermined low rating criteria
However, Bandaru et al. which is in the art of analyzing user generated content (see abstract and title) teaches
(1) embedded web scraper, (see paragraphs 0013 and 0035, Examiner’s note: teaches crawling information from a website).
(2) the source being a website, (see paragraphs 0013 and 0035, Examiner’s note: teaches crawling information from a website).
(3) a tokenization process, (see paragraphs 0014, 0040, 0054-0055, Examiner’s note: sentence segmentation through tokenization process).
(4) ranking the one or more text words based on a frequency of occurrence; and generating the review list profile based on one or more ranked words (see paragraph 0099, Examiner’s note: teaches based on the frequency and contextual relevance of such exception tokens they may bubble up to the point where they can be fed automatically into the domain noun collection).
And (5) extracting based on a predetermined low rating criteria (see paragraphs 0097, 0128, Examiner’s note: this limitation is so broad in the claims this could merely recite collecting information from professional and non-professional websites).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Grant in view of Bandaru et al. in view of Kuznetsova et al. with the aforementioned teachings from Bandaru et al. with the motivation of providing a known way to extract words from the reviews (see Bandaru et al. paragraphs 0014, 0040, 0054-0055), extract information from different sources (see Bandaru et al. paragraphs 0097 and 0128), and update the information over time as the machine learnings from collected data (see Bandaru et al. paragraph 0099), when extracting words from reviews (see Grant paragraphs 0041-0042, 0066, 0074-0075), extracting information from different sources (see Grant paragraph 0042-0043), and updating information over time are all known (see Grant paragraph 0070).
As per claim 18, Grant teaches
wherein identifying one or more customer identified-problems associated with the package comprises: text words identifies a packaging feature and determining the one or more customer identified-problems based on the one or more identified relationships. (see paragraphs 0041-0042, 0066, 0074-0075, Examiner’s note: teaches determining a risk score based on natural language processing of reviews like how often does an item get returned due to a reason of being a damaged package)
Grant does not expressly disclose identifying a list of frequently occurring text words used within the plurality of negative reviews categorized, wherein at least one text word of the list of frequently occurring text words identifies a feature; identifying one or more relationships between a first text word of the list of frequently occurring list of text words and another frequently occurring text word of the list of text words associated with the first text word;
However, Bandaru et al. which is in the art of analyzing user generated content (see abstract and title) teaches identifying a list of frequently occurring text words used within the plurality of negative reviews categorized, wherein at least one text word of the list of frequently occurring text words identifies a feature; identifying one or more relationships between a first text word of the list of frequently occurring list of text words and another frequently occurring text word of the list of text words associated with the first text word (see paragraphs 0057, 0062-0066, 0077-0078, 0099, 0101, 0117-0126, Examiner’s note: teaches here how the combination of multiple words can be used to determine sentiment where sentiment may be positive or negative and can also be based on frequency occurring or reoccurring words).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Grant in view of Bandaru et al. in view of Kuznetsova et al. with the aforementioned teachings from Bandaru et al. with the motivation of providing a known way to extract information from different sources based on context (see Bandaru et al. paragraphs 0057, 0062-0066, 0077-0078, 0117-0126), when extracting information from different sources based on content is known (see Grant paragraphs 0041-0042, 0066, 0074-0075).
As per claim 19, Grant teaches
determining package features (see paragraphs 0041-0042, 0066, 0074-0075, Examiner’s note: teaches determining a risk score based on natural language processing of reviews like how often does an item get returned due to a reason of being a damaged package)
Grant does not expressly teach calculating the frequency of words used in the reviews or more specifically as recited in the claims determining a number of occurrences for each identified relationship between each review feature of the list of frequently occurring review features and a frequently text word associated with a respective review feature
However, Bandaru et al. which is in the art of analyzing user generated content (see abstract and title) teaches calculating the frequency of words used in the reviews or more specifically as recited in the claims a number of occurrences for each identified relationship between each review feature of the list of frequently occurring review features and a frequently text word associated with a respective review feature (see paragraphs 0099-0102, Examiner’s note: updating over time based on the frequency of words).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Grant in view of Bandaru et al. in view of Kuznetsova et al. with the aforementioned teachings from Bandaru et al. with the motivation of providing a known way to extract and interpret information over time as based on information learned over time (see Bandaru et al. paragraphs 0099-0102), when extracting information from different sources based on content (see Grant paragraphs 0041-0042, 0066, 0074-0075) and learning information over time is known (see Grant paragraph 0070).
As per claim 21, Grant teaches A method comprising: (see paragraph 0007, Examiner’s note: method for identifying and communicating package delivery risks to reduce package damage during delivery).
scraping a plurality of customer reviews for a specific product from a user-identified source, wherein each customer review includes one or more text words that is used to express a customer's experience with the specific product based on predetermined extraction constraints for the specific product; identifying one or more packaging related reviews based on a packaging list profile and the plurality of customer reviews; identifying information related to packaging reviews based on wording of the one or more product related views; and package feature (see paragraphs 0041-0042, 0066, 0074-0075, Examiner’s note: teaches determining a risk score based on natural language processing of reviews like how often does an item get returned due to a reason of being a damaged package).
and providing one or more results of an evaluation of the package based on the customer identified problem. (see paragraph 0047-0048, Examiner’s note: best delivery practice, risk calculation, and communicating that results of evaluation
Grant does not expressly teach (1) the source is a website, (2) categorizing, using a natural language process, whether each of the one or more review related reviews is a negative review or a positive review, (3) identifying a customer identified-problem based a relationship between two frequently occurring text words found within the plurality of negative reviews categorized, wherein the two text words includes a first text word describing a review feature and a second text word that co-occurs in association with the first text word, and (4) extracting information based on a low negative rating.
However, Bandaru et al. which is in the art of analyzing user generated content (see abstract and title) teaches
(1) the source is a website (see paragraphs 0013 and 0035, Examiner’s note: teaches crawling information from a website).
(2) categorizing, using a natural language process, whether each of the one or more review related reviews is a negative review or a positive review (see paragraph 0077, 0172-0173, and Figure 16A, Examiner’s note: each review itself can be rated as positive, negative, or neutral sentiment using the same approach).
and (3) identifying a customer identified-problem based a relationship between two frequently occurring text words found within the plurality of negative reviews categorized, wherein the two text words includes a first text word describing a review feature and a second text word that co-occurs in association with the first text word; (see paragraphs 0057, 0062-0066, 0077-0078, 0099, 0101, 0117-0126, Examiner’s note: teaches here how the combination of multiple words can be used to determine sentiment where sentiment may be positive or negative and can also be based on frequency occurring or reoccurring words).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Grant in view of Bandaru et al. with the aforementioned teachings from Bandaru et al. with the motivation of providing a way to collect data from different websites (see Bandaru et al. paragraphs0013 and 0035) and another way to summarize the reviews (see Bandaru et al. paragraph 0077), when extracting information from different sources based on content (see Grant paragraphs 0041-0042, 0066, 0074-0075) and updating information over time (see Grant paragraph 0070) are both known.
Grant in view of Bandaru et al. does not expressly teach (4) extracting information based on a low negative rating.
However, Kuznetsova et al. which is in the art of automatic online review interpretation (see abstract) teaches (4) extracting information based on a low negative rating (see column 7 lines 23-42, Examiner’s note: extracting information for combined reviews based on being above or below a threshold).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Grant in view of Bandaru et al. with the aforementioned teachings from Kuznetsova et al. with the motivation of providing another known constraint to filter user reviews or ratings information to perform analysis on (see Kuznetsova et al. column 7 lines 23-42), when filtering user review information based on numerous different types of constraints (see Grant paragraphs 0041-0042 and Bandaru et al. paragraphs 0097 and 0128) is known in both the references of Grant and Bandaru et al.
Claim(s) 2 is rejected under 35 U.S.C. 103 as being unpatentable over Grant et al. (United States Patent Application Publication Number: US 2022/0101248) further in view of Bandaru et al. (United States Patent Application Publication Number: US 2008/0133488) further in view of Kuznetsova et al. (United States Patent Number: US 9, 405, 825) further in view of Bostick et al. (United States Patent Application Publication Number: US 2017/0148071).
As per claim 2, Grant et al. teaches
wherein the identifying, using the computer-based packaging evaluation system, the one or more packaging reviews comprises: splitting, using a process of the computer-based packing evaluation system, each customer review of the plurality of customer reviews into one or more segments of text words, wherein the one or more segments of text words includes an individual word or a phrase having two or more words; determining, using a process of the computer-based packaging system, words; and identifying, using the computer-based packaging evaluation system, one or more packaging related reviews based on a packaging list profile and the one or more segment of text words. (see paragraphs 0041-0042, 0066, 0074-0075, Examiner’s note: teaches determining a risk score based on natural language processing of reviews like how often does an item get returned due to a reason of being a damaged package).
Grant does not expressly teach (1) using a tokenization process and (2) determining, using a lemmatization process of the evaluation system, a base form of each word in the one or more segments of text words.
However, Bandaru et al. which is in the art of analyzing user generated content (see abstract and title) teaches (1) using a tokenization process (see paragraphs 0014, 0040, 0054-0055, Examiner’s note: sentence segmentation through tokenization process).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Grant in view of Bandaru et al. in view of Kuznetsova et al. with the aforementioned teachings from Bandaru et al. with the motivation of providing a known way to extract words from the reviews (see Bandaru et al. paragraphs 0014, 0040, 0054-0055), when extracting words from reviews is known (see Grant paragraphs 0041-0042, 0066, 0074-0075).
Grant in view of Bandaru et al. in view of Kuznetsova et al. does not expressly teach (2) determining, using a lemmatization process of the evaluation system, a base form of each word in the one or more segments of text words.
However, Bostick et al. which is in the art of online reviews (see abstract and title) teaches (2) determining, using a lemmatization process of the evaluation system, a base form of each word in the one or more segments of text words (see paragraph 0021, Examiner’s note: NPL machine learning techniques that break down the ORR (which is the online product or service reviews and rankings, see paragraph 0019) to the lemma level like walking, walks, walked all have a lemma of walk).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Grant in view of Bandaru et al. in view of Kuznetsova et al. with the aforementioned teachings from Bostick et al. with the motivation of using a commonly known NLP technique to understand a body of text and determine sentiment (see Bostick et al paragraph 0021), when using NPL techniques to understand a body of text is known (see Grant paragraphs 0074-0075).
Claim(s) 14 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Grant et al. (United States Patent Application Publication Number: US 2022/0101248) further in view of Bandaru et al. (United States Patent Application Publication Number: US 2008/0133488) further in view of Kuznetsova et al. (United States Patent Number: US 9, 405, 825) further in view of Stensmo (United States Patent Application Publication Number: US 2002/0194158).
As per claim 14, Grant teaches
further comprising Packaging features (see paragraphs 0041-0042, 0066, 0074-0075, Examiner’s note: teaches determining a risk score based on natural language processing of reviews like how often does an item get returned due to a reason of being a damaged package)
Grant does not expressly teach (1) further comprising pruning, to reduce the number of identified relations below a predetermined threshold and (2) one or more identified relationships between each review feature of the list of frequently occurring review features and a frequently text word associated with a respective review feature
However, Bandaru et al. which is in the art of analyzing user generated content (see abstract and title) teaches (2) one or more identified relationships between each review feature of the list of frequently occurring review features and a frequently text word associated with a respective review feature (see paragraphs 0099-0102, Examiner’s note: updating over time based on the frequency of words).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Grant in view of Bandaru et al. in view of Kuznetsova et al. with the aforementioned teachings from Bandaru et al. with the motivation of providing a known way to extract and interpret information over time as based on information learned over time (see Bandaru et al. paragraphs 0099-0102), when extracting information from different sources based on content (see Grant paragraphs 0041-0042, 0066, 0074-0075) and learning information over time (see Grant paragraph 0070) are both known.
Grant in view of Bandaru et al. does not expressly teach (1) further comprising pruning, to reduce the number of identified relations below a predetermined threshold
However, Stensmo which is in the art of using models to predict text classification, feature extraction, related words, query expansion, and text classification (see paragraph 0026) teaches pruning or more specifically (1) further comprising pruning, to reduce the number of identified relations below a predetermined threshold (see paragraph 0064-0067, Examiner’s note: teaches pruning based on a threshold to reduce how much memory is used).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Grant in view of Bandaru et al. in view of Kuznetsova et al. with the aforementioned teachings from Stensmo with the motivation of providing a common way to reduce memory constraints in machine learning to predict text classification, feature extraction, related words, query expansion, and text classification (see Stensmo paragraphs 0026 and 0064-0067), when calculating online reviews through feature extraction, text classification, and related words are known (see Grant paragraphs 0041-0042, 0066, 0074-0075).
As per claim 20, Grant teaches
further comprising Packaging features (see paragraphs 0041-0042, 0066, 0074-0075, Examiner’s note: teaches determining a risk score based on natural language processing of reviews like how often does an item get returned due to a reason of being a damaged package)
Grant does not expressly teach (1) further comprise pruning, to reduce the number of identified relations below a predetermined threshold and (2) one or more identified relationships between each review feature of the list of frequently occurring review features and a frequently text word associated with a respective review feature
However, Bandaru et al. which is in the art of analyzing user generated content (see abstract and title) teaches (2) one or more identified relationships between each review feature of the list of frequently occurring review features and a frequently text word associated with a respective review feature (see paragraphs 0099-0102, Examiner’s note: updating over time based on the frequency of words).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Grant in view of Bandaru et al. in view of Kuznetsova et al. with the aforementioned teachings from Bandaru et al. with the motivation of providing a known way to extract and interpret information over time based on information learned over time (see Bandaru et al. paragraphs 0099-0102), when extracting information from different sources based on content is known (see Grant paragraphs 0041-0042, 0066, 0074-0075).
Grant in view of Bandaru et al. in view of Kuznetsova et al. does not expressly teach (1) further comprising pruning, to reduce the number of identified relations below a predetermined threshold
However, Stensmo which is in the art of using models to predict text classification, feature extraction, related words, query expansion, and text classification (see paragraph 0026) teaches pruning or more specifically (1) further comprising pruning, to reduce the number of identified relations below a predetermined threshold (see paragraph 0064-0067, Examiner’s note: teaches pruning based on a threshold to reduce how much memory is used).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Grant in view of Bandaru et al. in view of Kuznetsova et al. with the aforementioned teachings from Stensmo with the motivation of providing a common way to reduce memory constraints in machine learning to predict text classification, feature extraction, related words, query expansion, and text classification (see paragraphs 0026 and 0064-0067), when calculating online reviews through feature extraction, text classification, and related words are known (see Grant paragraphs 0041-0042, 0066, 0074-0075).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Sainath et al. (United States Patent Application Publication Number: US 2012/0078834) teaches pruning is accomplished by removing words from the vocabulary if the total number of occurrences in the training was less than a certain frequency threshold (see paragraph 0026)
Burgess et al. (United States Patent Application Publication Number: US 2009/0063247) teaches a system for collecting and classifying the opinions on products (see abstract)
Anshul (United States Patent Application Publication Number: US 2010/0049590) teaches a system for semantic analysis of unstructured data of customer feedback data (see abstract and title)
Wilson et al. (United States Patent Number: US 9,552,553) teaches customer reviews may include item arrived in excessive packaging (see column 6 lines 15-35)
Figueroa et al. (United States Patent Number: US 11,551,241) teaches a system for digital shelf display performance that includes packaging reviews (see column 2 lines 5-15)+
Willard et al. (United States Patent Application Publication Number: US 2016/0180414) teaches ratings thresholds used to evaluate reviews (see paragraph 0089)
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/KIERSTEN V SUMMERS/Primary Examiner, Art Unit 3626