DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Claims 1, 3-7, 9, 11-15, and 17-20 are pending.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1, 3-7, 9, 11-15, and 17-20 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.
The term “better” in claims 1, 3, 9, 11, 17, and 18 is a relative term which renders the claim indefinite. The term “better” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Appropriate correction is required.
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, 3-7, 9, 11-15, and 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Step 1 (The Statutory Categories): Is the claim to a process, machine, manufacture, or composition of matter? MPEP 2106.03.
Per Step 1, claim 1 is to a method (i.e., a process), claim 9 to a system (i.e., a machine), and claim 17 to a computer program product (i.e., a manufacture). Thus, the claims are directed to statutory categories of invention. However, the claims are rejected under 35 U.S.C. 101 because they are directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The analysis proceeds to Step 2A Prong One.
(Examiner notes that the computer readable storage medium, which is claimed as part of the computer program product in claim 17, is defined by applicant “not to be construed as storage in the form of transitory signals per se” in [0044] of the specification as filed. Accordingly, the claim, which describes a non-transitory computer readable storage medium, meets the threshold at Step 1.)
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon? MPEP 2106.04.
The abstract idea of claims 1, 9, and 17 is (claim 1 being representative):
evaluating return data, received from a return channel, against a threshold,
wherein the return data includes attribute data of an originally-obtained product, attribute data of a new product, and a reasoning specifying that a return of the originally-obtained product is based on the new product having one or more attributes better than one or more attributes of the originally-obtained product;
validating, based upon the threshold being satisfied, the return data;
cognitive processing, based upon the threshold being satisfied, the return data to generate a return insight;
generating, based upon the return insight, a corrective action,
wherein the generating of the corrective action includes adjusting a product attribute of the originally-obtained product; and
associating a negative reward with the generated corrective action based on the generated corrective action that is not implemented within a specific period of time; and
updating the threshold based on the associating of the negative reward with the generated corrective action.
The abstract idea steps italicized above are those which could be performed mentally, including with pen and paper. The steps describe, at a high level, evaluating return data against a threshold, validating it, generating a return insight and corresponding corrective action, and updating the threshold. These are steps an administrator could perform, using pencil and paper. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, including observations, evaluations, judgements, and/or opinions, then it falls within the Mental Processes – Concepts Performed in the Human Mind grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additionally and alternatively, the abstract idea steps italicized above describe a return process for a purchased product, which constitutes a process that, under its broadest reasonable interpretation, covers commercial activity. This is further supported by [0003] of applicant’s specification as filed. If a claim limitation, under its broadest reasonable interpretation, covers commercial interactions, including contracts, legal obligations, advertising, marketing, sales activities or behaviors, and/or business relations, then it falls within the Certain Methods of Organizing Human Activity – Commercial or Legal Interactions grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additionally and alternatively, the abstract idea steps italicized above describe the rules or instructions pertaining to a return process for a purchased product, which constitutes a process that, under its broadest reasonable interpretation, covers managing personal behavior relationships, interactions between people. This is further supported by [0003] of applicant’s specification as filed. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior relationships, interactions between people, including social activities, teaching, and/or following rules or instructions, then it falls within the Certain Methods of Organizing Human Activity – Managing Personal Behavior Relationships, Interactions Between People grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? MPEP 2106.04.
This judicial exception is not integrated into a practical application because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP 2106.05(f).
Claim 1 recites the following additional elements: computer-implemented; training a neural network; using the neural network; electronic display; training the neural network using feedback generated based upon the generated corrective action, wherein the training of the neural network includes.
Claim 9 recites the following additional elements: computer hardware system; hardware processor; training a neural network; using the neural network; electronic display; training the neural network using feedback generated based upon the generated corrective action, wherein the training of the neural network includes.
Claim 17 recites the following additional elements: computer program product; computer readable storage medium having stored therein program code for training a neural network; computer hardware system; using the neural network; electronic display; training the neural network using feedback generated based upon the generated corrective action, wherein the training of the neural network includes.
These elements are merely instructions to apply the abstract idea to a computer, per MPEP 2106.05(f). Applicant has only described generic computing elements in their specification, as seen in [0040]-[0057] of applicant’s specification as filed, for example.
Examiner interprets the neural network and associated training features, described in [0020] and [0035] of applicant’s specification as filed, as additional elements. MPEP 2106.05(f) is explicit that simply using other machinery as a tool also amounts to no more than merely applying the abstract idea to a computer, especially when claimed in a solution-oriented manner:
(1) Whether 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". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). In contrast, claiming a particular solution to a problem or a particular way to achieve a desired outcome may integrate the judicial exception into a practical application or provide significantly more. See Electric Power, 830 F.3d at 1356, 119 USPQ2d at 1743.
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(2) Whether the claim invokes 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. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
In this case, the neural network and associated training features, which are merely being used to facilitate the tasks of the abstract idea, provide nothing more than a results-oriented solution that lacks detail of the mechanism for accomplishing the result and are equivalent to the words “apply it,” per MPEP 2106.05(f).
Further, the combination of these elements is nothing more than a generic computing system applied to the tasks of the abstract idea. Because the additional elements are merely instructions to apply the abstract idea to a generic computing system, they do not integrate the abstract idea into a practical application, when viewed in combination. See MPEP 2106.05(f).
Therefore, per Step 2A Prong Two, the additional elements, alone and in combination, do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
Step 2B (The Inventive Concept): Does the claim recite additional elements that amount to significantly more than the judicial exception? MPEP 2106.05.
Step 2B involves evaluating the additional elements to determine whether they amount to significantly more than the judicial exception itself.
The examination process involves carrying over identification of the additional element(s) in the claim from Step 2A Prong Two and carrying over conclusions from Step 2A Prong Two pertaining to MPEP 2106.05(f).
The additional elements and their analysis are therefore carried over: applicant has merely recited elements that facilitate the tasks of the abstract idea, as described in MPEP 2106.05(f).
Further, the combination of these elements is nothing more than a generic computing system applied to the tasks of the abstract idea. When the claim elements above are considered, alone and in combination, they do not amount to significantly more.
Therefore, per Step 2B, the additional elements, alone and in combination, are not significantly more. The claims are not patent eligible.
The analysis takes into consideration all dependent claims as well:
Dependent claims 3-7, 11-15, and 18-20 include additional abstract steps and/or information that narrow the abstract idea. Some of the dependent claims include further additional elements (claims 5, 7, 13, 15, and 19-20: electronic and/or electronically). Similar to above, these additional elements are merely being used to facilitate the tasks of the abstract idea and equivalent to “apply it,” per MPEP 2106.05(f). Whether viewed alone or in combination, this does not integrate the narrowed abstract idea into practical application and/or add significantly more.
Accordingly, claims 1, 3-7, 9, 11-15, and 17-20 are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant's arguments filed 2/3/26 have been fully considered. Examiner’s response follows, with applicant’s page numbers used for consistency.
Claim Objections
Applicant is thanked for their amendments overcoming the previous claim objections, which are withdrawn.
Claim Rejections - 35 USC § 101
On pages 11-15, applicant provides remarks regarding the rejections under 35 USC § 101. While well taken, they are not persuasive.
Applicant first offers:
Regarding Prong One of Step 2A, the Applicant respectfully submits that amended independent claim 1 of the instant application recites, for example, the features of: "generating, using the neural network and based upon the return insight, a corrective action ... the generating of the corrective action includes adjusting an electronic display of a product attribute of the originally-obtained product ... training the neural network using feedback generated based upon the generated corrective action, wherein the training of the neural network includes: associating a negative reward with the generated corrective action based on the generated corrective action that is not implemented within a specific period of time; and updating, using the neural network, the threshold based on the association of the negative reward with the generated corrective action".
The Applicant submits that at least the above-recited claim features are inextricably tied to a machine and a computer technology. The method of the present disclosure improves the technological field of neural network training by using return data associated with a "found a better item" exchange to dynamically train a neural network for an improved threshold and corrective action determination. Therefore, the claimed features cannot be considered as a mental process or Certain Methods of Organizing Human Activity.
At least for the above-mentioned reasons, the Applicant respectfully submits that the amended independent claim 1 meets standards for patent eligibility under prong one of Step 2A of 2019 Revised Patent Subject Matter Eligibility Guidance. Therefore, the claims are not directed to the alleged abstract idea.
Examiner first notes that Step 2A Prong One asks the question: do the claims recite an abstract idea? The directed to inquiry is performed at Step 2A Prong Two. Examiner’s position is that an abstract idea is recited. Further, it appears that applicant has conflated the abstract idea, considered at Step 2A Prong One, with the additional elements, considered at Step 2A Prong Two and Step 2B. Examiner’s position is that the tasks of the abstract idea are merely being facilitated by generic computing elements, including a neural network. Applicant’s remarks to the contrary are not persuasive.
Applicant continues:
Regarding Prong Two of Step 2A of the 2019 Revised Patent Subject Matter Eligibility Guidance, even if one were to arrive at a conclusion satisfying the Prong one of such analysis, assuming arguendo, to which the Applicant does not concede, the Applicant submits that the alleged abstract idea is integrated into a practical implementation.
The Applicant's disclosure describes, for example, "[t]he present invention relates to neural network training, and more specifically, to using exchange data to train a neural network for improved threshold and action item determination ... [n]eural networks, for example, have been used in commercial settings, such as in the prediction of retail sales. However, the use of neural networks in the opposite of a sale (i.e., a return) is limited... what is needed is an improved neural network that could timely and correctly suggest corrective actions when an exchange is being requested based upon 'found a better item' being the stated reason for the exchange ... [a]s conventionally known, the quality of the machine learning model (e.g., a neural network) being trained is dependent upon the quantity and quality of the data in the dataset. In 120, the data in the dataset is prepared, and this may involve a wide variety of different operations ... [t]he return data 315 from the return channels 310A-B is received, processed, and stored by data collection/storage 320. For example ... [t]he purpose of the data validation operations can include both confirming that the return data 315 accurately reflects the difference between the originally-obtained product and the new product as well as supplementing the return data 315 with additional attributes/features that distinguish the originally-obtained product from the new product ... the feedback 395 does relate to the effectiveness of the corrective action communicated to either the manufacturer 385 or the source channel 375". See at [0001], [0003], [0017], [0028], [0030], and [0034] of the Specification as originally filed(emphasis added).
As shown in the Specification above, the present invention provides solutions to the technical problems:
By evaluating return data against a threshold before validation and cognitive processing, the claimed method ensures that only return data associated with a "found a better item" reasoning is processed by the neural network. This threshold based gating reduces unnecessary computation, improves data quality used for training, and increases the relevance of neural network learning in return-based scenarios.
The claimed method advantageously generates corrective actions. By readjusting, based on corrective actions, an electronic display of a product attribute of the originally-obtained product, the system enables actionable responses that are directly aligned with the identified return insight.
Training the neural network using feedback generated from whether corrective actions are associated with a negative reward, enables the neural network to learn which corrective actions are effective in practice, improving future action selection and system performance.
Further, updating the threshold based on negative rewards allows the system to dynamically adapt its sensitivity to return data over time. Thresholds associated with effective corrective actions are reinforced, while thresholds associated with ineffective actions are penalized, resulting in a self-optimizing mechanism that continuously improves decision accuracy.
Accordingly, the Applicant has shown teaching in the Specification that describes a practical implementation and how the technology is improved and has thus established a clear nexus between the claim language and the practical implementation of the alleged judicial exception, and improvements in the technology.
Further, the features of the amended independent claim 1 add specific limitations that are not well-understood, routine, conventional activity in the field. Therefore, the Applicant respectfully submits that taking all the claim elements of amended independent claim 1 individually, and in combination, amended independent claim 1 as a whole amounts to significantly more than the alleged abstract idea.
At least, for these reasons, the Applicant respectfully submits that the amended independent claim 1 meets the standard for patent eligibility under 35 U.S.C. § 101.
At Step 2A Prong Two and Step 2B, the questions being asked are 1) do the additional elements integrate the abstract idea into practical application or 2) add significantly more? Examiner’s position is that they do not. The claims do not reflect a technological solution to any technological problem, which the specification fails to articulate. Examiner queries applicant: what is the shortcoming of existing neural network architecture?
Instead, applicant has taken generic computing elements and used them to facilitate the tasks of the abstract idea. While applicant may potentially arrive at a novel abstract idea, this is not the same as a claim being patent eligible. Further, applicant has not claimed or described the neural network in any real detail. Rather, applicant has used results-oriented language, utilizing the neural network and training steps in an “off-the-shelf” manner.
Accordingly, examiner maintains the rejections under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 103
Regarding the rejections under 35 USC § 103, Applicant’s clarifying remarks and amendments are persuasive. These rejections are withdrawn. In an updated search, examiner identified the following references, which, while generally relevant to the field of endeavor, stop short of the specificity required by the claim:
US 5774868, which teaches: An automated sales promotion selection system uses neural networks to identify promising sales promotions based on recent customer purchases. The system includes a customer information device that receives customer data relating to customer purchases of items from an inventory of items, a central processing unit having a sales promotion neural network and a storage unit containing a plurality of item identifiers comprising potential customer purchases of additional items from the inventory, wherein the sales opportunity neural network responds to customer data received from the customer information device by determining if one or more of the item identifiers in the storage unit corresponds to an item likely to be purchased by one of the customers, and an output device that receives the item identifiers of the likely purchases determined by the sales promotion neural network and produces a sales promotion relating to at least one of the item identifiers.
US 6430539, which teaches: Predictive modeling of consumer financial behavior is provided by application of consumer transaction data to predictive models associated with merchant segments. Merchant segments are derived from consumer transaction data based on co-occurrences of merchants in sequences of transactions. Merchant vectors representing specific merchants are clustered to form merchant segments in a vector space as a function of the degree to which merchants co-occur more or less frequently than expected. Each merchant segment is trained using consumer transaction data in selected past time periods to predict spending in subsequent time periods for a consumer based on previous spending by the consumer. Consumer profiles describe summary statistics of consumer spending in and across merchant segments. Analysis of consumers associated with a segment identifies selected consumers according to predicted spending in the segment or other criteria, and the targeting of promotional offers specific to the segment and its merchants.
US 20110213651, which teaches: A computer-implemented method for providing customer recommendations for a product is disclosed. For each target product for which a customer recommendation is desired, one or more customers likely to purchase the target product are identified using a mathematical model that considers customers' prior purchases of products that are similar or related to the target product.
Accordingly, the rejections under 35 USC § 103 are withdrawn.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 5774868, which teaches: An automated sales promotion selection system uses neural networks to identify promising sales promotions based on recent customer purchases. The system includes a customer information device that receives customer data relating to customer purchases of items from an inventory of items, a central processing unit having a sales promotion neural network and a storage unit containing a plurality of item identifiers comprising potential customer purchases of additional items from the inventory, wherein the sales opportunity neural network responds to customer data received from the customer information device by determining if one or more of the item identifiers in the storage unit corresponds to an item likely to be purchased by one of the customers, and an output device that receives the item identifiers of the likely purchases determined by the sales promotion neural network and produces a sales promotion relating to at least one of the item identifiers.
US 6430539, which teaches: Predictive modeling of consumer financial behavior is provided by application of consumer transaction data to predictive models associated with merchant segments. Merchant segments are derived from consumer transaction data based on co-occurrences of merchants in sequences of transactions. Merchant vectors representing specific merchants are clustered to form merchant segments in a vector space as a function of the degree to which merchants co-occur more or less frequently than expected. Each merchant segment is trained using consumer transaction data in selected past time periods to predict spending in subsequent time periods for a consumer based on previous spending by the consumer. Consumer profiles describe summary statistics of consumer spending in and across merchant segments. Analysis of consumers associated with a segment identifies selected consumers according to predicted spending in the segment or other criteria, and the targeting of promotional offers specific to the segment and its merchants.
US 20110213651, which teaches: A computer-implemented method for providing customer recommendations for a product is disclosed. For each target product for which a customer recommendation is desired, one or more customers likely to purchase the target product are identified using a mathematical model that considers customers' prior purchases of products that are similar or related to the target product.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN SAMUEL WASAFF whose telephone number is (571)270-5091. The examiner can normally be reached Monday through Friday 8:00 am to 6:00 pm.
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, SARAH MONFELDT can be reached at (571) 270-1833. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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JOHN SAMUEL WASAFF
Primary Examiner
Art Unit 3629
/JOHN S. WASAFF/Primary Examiner, Art Unit 3629