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 Claims
Claim(s) 1-20 have been examined.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claims recite a judicial exception which is not integrated into a practical application and the claims lack an inventive concept.
Step 1 is the first inquiry into eligibility analysis and asks whether the claims are directed to a statutory category. In this instance, the answer must be in the affirmative because they recite a method and system.
Step 2A prong 1 is the next step in the eligibility analyses and asks whether the claimed invention recites a judicial exception. In this instance, the claims recite the following limitations which comprise the abstract idea:
collecting history information, wherein the history information comprises one or more episodes from one or more customers, wherein each episode comprises one or more items and timing information, wherein a subset of the one or more episodes comprises a label identifying a mission;
determining, the label identifying the mission for each of the one or more episodes for a customer of the one or more customers;
determining a pattern for the label of each of the one or more episodes for the customer based on a schedule of historical shopping missions ordered in a timetable, wherein the historical shopping missions are identified from the one or more episodes for the customer;
predicting a future episode for the customer based on the pattern, wherein the predicting comprises predicting at least one of a time or a type of the future episode based on the schedule;
This is an abstract idea because it is a certain method of organizing human activity because it involves commercial interactions such as sales and/or marketing behaviors and/or activities.
Step 2A prong 2 is the next step in the eligibility analyses and looks at whether the abstract idea is integrated into a practical application. This requires an additional element or combination of additional elements in the claims to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception.
In this instance, the claims recite the additional elements such as:
training a natural language processor on subset of the one or more episodes to generate the label for each episode based on the one or more items;
using a natural language processor
However, these elements do not amount to an improvement in the functioning of a computer or any other technology or technical field, apply the judicial exception with, or by use of, a particular machine, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
In addition, the recitations of the additional limitations are recited at a high level of generality and also do not amount to an improvement in the functioning of a computer or any other technology or technical field, apply the judicial exception with, or by use of, a particular machine, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
The dependent claims also fail to recite elements which amount to an improvement in the functioning of a computer or any other technology or technical field, apply the judicial exception with, or by use of, a particular machine, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. For example, claims 2, 4, 7-11 is directed at the abstract idea. Claims 5-6 are directed to a mathematical formula. But even if these dependent claims were not directed to the abstract idea, they do not amount to an integration according to any one of the considerations above.
Step 2B is the next step in the eligibility analyses and evaluates whether the claims recite additional elements that amount to an inventive concept (i.e., “significantly more”) than the recited judicial exception. According to Office procedure, revised Step 2A overlaps with Step 2B, and thus, many of the considerations need not be re-evaluated in Step 2B because the answer will be the same.
In Step 2A, several additional elements were identified as additional limitations:
training a natural language processor on subset of the one or more episodes to generate the label for each episode based on the one or more items;
using a natural language processor
These additional limitations, including the limitations in the dependent claims, do not amount to an inventive concept because they are recited at a high level of generality and also do not amount to an improvement in the functioning of a computer or any other technology or technical field, apply the judicial exception with, or by use of, a particular machine, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
In addition, they were already analyzed under Step 2A and did not amount to a practical application of the abstract idea.
Therefore, the claims lack one or more limitations which amount to an inventive concept in the claims.
For these reasons, the claims are rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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.
Claims 1-4, 7-8, 10, 12, 15-16, 20 is/are rejected under 35 U.S.C. 103 as being obvious over Tavernier (US 10,706,450) in view of High (US 2017/0262926).
Referring to Claim 1, Tavernier teaches a computer-implemented method comprising:
collecting history information, wherein the history information comprises one or more episodes from one or more customers, wherein each episode comprises one or more items and timing information (see Tavernier Col. 16 line 48 to Col. 17 line 15), wherein a subset of the one or more episodes comprises a label identifying a mission (see Tavernier Col. 1 lines 61-67, auto labeling a search query of a user to define a shopping “mission”);
training a natural language processor on subset of the one or more episodes to generate the label for each episode based on the one or more items (see Tavernier Col. 2 lines 42-48);
determining, using the natural language processor, the label identifying the mission for each of the one or more episodes for a customer of the one or more customers (see Tavernier Col. 1 lines 60-67);
determining a pattern for the label of each of the one or more episodes for the customer (see Tavernier Col. 6 lines 2-11 and Col. 8 lines 32-41);
predicting a future episode for the customer based on the pattern (see Tavernier Col. 5 lines 34-45).
Tavernier does not teach that the pattern is based on the schedule of the historical shopping missions ordered in a timetable, wherein the historical shopping missions are identified from the one or more episodes for the customer, or that the predicting comprises predicting at least one of a time or type of the future episode based on the schedule. However, High teaches storing a shopper’s past purchase receipts, identifying the product category associated with each purchase, examining repeated purchase dates, and determining an a time period between purchases of an item or category of items (see High ¶¶0053-55). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine these references because the results would be predictable. Specifically, Tavernier would continue to teach determining a pattern and predicting future episodes, except that now the pattern would be based on a schedule of historical shopping missions and the prediction would include predicting a time or type of the future episode based on the schedule, according to the teachings of High. This is a predicable result of the combination.
Referring to Claim 2, Tavernier teaches the computer-implemented method of claim 1, further comprising generating a personalized marketing message for the customer based on the predicted future episode (see Tavernier Col. 5 lines 34-45).
Referring to Claim 7, the combination teaches the computer-implemented method of claim 1, wherein the label identifying the mission comprises one of a plurality of mission categories based on an approximate cost of a basket associated with the episode (see High ¶0061).
Referring to Claim 8, the combination teaches the computer-implemented method of claim 1, further comprising automatically generating a shopping list for the customer based on the predicted future episode (see High ¶¶0058-59).
Referring to Claim 10, the combination teaches the computer-implemented method of claim 1, wherein the history information further comprises identifying information associated with the customer (see Tavernier Col. 16 lines 47-60).
Referring to Claim 12, the combination teaches the computer-implemented method of claim 2, wherein generating the personalized marketing message comprises at least one of transmitting an email, SMS message, application push, webpage push, webpage serve request, inbound phone call, outbound phone call, or mailer (see High ¶0074).
Referring to Claim 3-4, 15-16, 18, 20, these claims are similar to claims 1-2, 7-8, 10, and 12 and therefore rejected under the same reasons and rationale.
Claims 5, 9, 13, 17 is/are rejected under 35 U.S.C. 103 as being obvious over Tavernier (US 10,706,450) in view of High (US 2017/0262926) in further view of Reference X (see PTO-892).
Referring to Claim 5, the combination teaches the computer-implemented method of claim 1, but does not teach wherein training the natural language processor comprises training a Latent Dirichlet Allocation (LDA) model on the subset of the one or more episodes to generate a segmentation of shopping missions. However, Reference X teaches training an LDA model on transaction data from retail locations in order to generate a segmentation of purchase behavior and assigning latent-purchase behavior topics to purchase transaction documents (see Reference X pages 435-436). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine these references because the results would be predictable. Specifically, the combination would continue to teach training the natural language processor except that it would result in training a Latent Dirichlet Allocation (LDA) model on the subset of the one or more episodes to generate a segmentation of shopping missions by applying the teachings of Reference X. This is a predictable result of the combination.
Referring to Claim 9, the combination teaches the computer-implemented method of claim 1, but does not teach wherein the natural language processor is trained to generate a representation of a shopping mission of each episode by treating each episode as a document and each item as a word within the document. However, Reference X teaches representing each retail transaction as a document and treating each product category within the transaction as a word, with LDA assigning latent purchase-behavior topics to the transaction documents (see Reference U page 436). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine these references because the results would be predictable. Specifically, the combination would continue to teach training the natural language processor except that it would result in generating a representation of a shopping mission of each episode by treating each episode as a document and each item as a word within the document by applying the teachings of Reference X. This is a predictable result of the combination. This is a predictable result of the combination
Referring to Claim 13 and 17, this claim is similar to claim 5 and 9 and therefore rejected under the same reasons and rationale.
Claims 6 and 14 is/are rejected under 35 U.S.C. 103 as being obvious over Tavernier (US 10,706,450) in view of High (US 2017/0262926) in further view of Reference W (see PTO-892).
Referring to Claim 6, the combination teaches the computer-implemented method of claim 1 but does not teach calculating a term frequency-inverse document frequency (TFIDF) for each episode, wherein the TFIDF weights items common to all customers as less important in comparison to rarer items. However, Reference W teaches calculating TFIDF for shopping carts, explaining how raw co-occurrence counts favor items that frequently occur across all shopping carts, and it also teaches that the TFIDF formula boosts items having a lower frequency across all the shopping carts on the marketplace (see Reference W pages 790-791). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine these references because the results would be predictable. Specifically, the combination would continue to teach determining labels for shipping missions and predicting future episodes, except that now the combination would further teach the calculation of TFIDF for each episode by applying the teachings of Reference W. This is a predictable result of the combination.
Referring to Claim 14, this claim is similar to claim 6 and therefore rejected under the same reasons and rationale.
Claims 11 and 19 is/are rejected under 35 U.S.C. 103 as being obvious over Tavernier (US 10,706,450) in view of High (US 2017/0262926) in further view of Stevens (US 2011/0238522).
Referring to Claim 11, the combination teaches the computer-implemented method of claim 1, but do not teach comparing an approximated episode comprising one or more items to an actual episode derived from transactional data to iteratively improve a model for predicting a future episode. However, Stevens teaches comparing a generated predictive smart list with the actual shopping list generated at checkout and modifying a shopping list trend based on the comparison (see Stevens ¶0042), using the modified shopping list trend to predict the next shopping list, and modifying the shopping list trend as needed (see Stevens ¶0049). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine these references because the results would be predictable. Specifically, the combination would continue to teach determining labels for shipping missions and predicting future episodes, except that now the combination would further teach comparing an approximated episode comprising one or more items to an actual episode derived from transactional data to iteratively improve a model for predicting a future episode by applying the teachings of Stevens. This is a predictable result of the combination.
Referring to Claim 19, this claim is similar to claim 11 and therefore rejected under the same reasons and rationale.
Remarks
Additional prior art relevant to the application claims but not relied upon includes:
KARMAKAR (US 2020/0043022) teaches AI for generating hierarchical data structures.
YOON (US 2019/0179915) teaches recommending items using metadata.
Reference U (see PTO-892) teaches shopping missions.
In response to the applicant’s arguments regarding the prior art, the amended limitations necessitated additional prior art which is discussed above.
In response to the applicant’s arguments regarding the rejection under 35 U.S.C. 101, the applicant argues on page 7 of the remarks that the amended claims are analogous to example 39 of the SME and therefore should be found eligible. The examiner respectfully disagrees. Example 39 did not contain an abstract idea, however, the claims at hand do contain an abstract idea. Therefore, the claims in the present application are not analogous to example 39 in the SME. For these reasons, the applicant’s arguments are not persuasive.
In response to the applicant’s arguments regarding the rejection under 35 U.S.C. 101, the applicant argues on page 7 of the remarks that the amended claims provide a technical solution because the claims recite using natural language processing with temporal pattern analysis. The examiner respectfully disagrees. Leaving aside for the time being whether this in fact is a technical solution, any technical solution must be directed at an underlying problem that specifically arose because of the creation of computers and/or the internet (e.g., DDR). The applicant has not identified such a problem, nor any problem for that matter. Nor has the applicant provided support for an alleged improvement. For these reasons, the applicant’s arguments are not persuasive.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW E ZIMMERMAN whose telephone number is (571)270-5278. The examiner can normally be reached 8-4pm M-T, 8-12pm W.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Marissa Thein can be reached at (571)272-6764. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MATTHEW E ZIMMERMAN/Primary Examiner, Art Unit 3688
Prosecution Insights